{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "initial_id",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:28.537558Z",
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    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sys.version_info(major=3, minor=10, micro=14, releaselevel='final', serial=0)\n",
      "matplotlib 3.10.0\n",
      "numpy 1.26.4\n",
      "pandas 2.2.3\n",
      "sklearn 1.6.0\n",
      "torch 2.5.1+cu124\n",
      "cuda:0\n"
     ]
    }
   ],
   "source": [
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import sklearn\n",
    "import pandas as pd\n",
    "import os\n",
    "import sys\n",
    "import time\n",
    "from tqdm.auto import tqdm\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "\n",
    "print(sys.version_info)\n",
    "for module in mpl, np, pd, sklearn, torch:\n",
    "    print(module.__name__, module.__version__)\n",
    "\n",
    "device = torch.device(\"cuda:0\") if torch.cuda.is_available() else torch.device(\"cpu\")\n",
    "print(device)\n",
    "\n",
    "seed = 42"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c29f1d6fe09a9955",
   "metadata": {},
   "source": [
    "# 数据加载\n",
    "\n",
    "- 采用WMT16的德语和英语平行语料库，数据集主页：[WMT16](https://www.statmt.org/wmt16/multimodal-task.html#task1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d047d1eed0336fbe",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:28.942222Z",
     "start_time": "2025-02-06T14:04:28.938639Z"
    },
    "ExecutionIndicator": {
     "show": true
    },
    "execution": {
     "iopub.execute_input": "2025-02-06T15:33:17.250639Z",
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     "shell.execute_reply": "2025-02-06T15:33:17.252380Z",
     "shell.execute_reply.started": "2025-02-06T15:33:17.250621Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# # 调用脚本文件\n",
    "# !pip install sacremoses\n",
    "# !sh data_multi30k.sh wmt16 wmt16_cut de en"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a930b5668e422750",
   "metadata": {},
   "source": [
    "## Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c0f0f4781f5e042f",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:29.020273Z",
     "start_time": "2025-02-06T14:04:29.011246Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-06T15:33:17.253528Z",
     "iopub.status.busy": "2025-02-06T15:33:17.253374Z",
     "iopub.status.idle": "2025-02-06T15:33:17.260074Z",
     "shell.execute_reply": "2025-02-06T15:33:17.259599Z",
     "shell.execute_reply.started": "2025-02-06T15:33:17.253513Z"
    }
   },
   "outputs": [],
   "source": [
    "from pathlib import Path\n",
    "from torch.utils.data import Dataset, DataLoader\n",
    "\n",
    "\n",
    "class LangPairDataset(Dataset):\n",
    "    def __init__(self, mode=\"train\", max_length=128,\n",
    "                 overwrite_cache=False, data_dir=\"wmt16\"):\n",
    "        self.data_dir = Path(data_dir)\n",
    "        cache_path = self.data_dir / \".cache\" / f\"de2en_{mode}_{max_length}.npy\"\n",
    "\n",
    "        if overwrite_cache or not cache_path.exists():\n",
    "            # parents=True：如果父目录不存在，会自动创建所有必要的父目录。\n",
    "            # exist_ok=True：如果目录已经存在，不会抛出错误。\n",
    "            cache_path.parent.mkdir(parents=True, exist_ok=True)\n",
    "            # 读取源语言文件所有行\n",
    "            with open(self.data_dir / f\"{mode}_src.bpe\", \"r\", encoding=\"utf8\") as f:\n",
    "                self.src = f.readlines()\n",
    "            # 读取目标语言文件所有行\n",
    "            with open(self.data_dir / f\"{mode}_trg.bpe\", \"r\", encoding=\"utf8\") as f:\n",
    "                self.trg = f.readlines()\n",
    "\n",
    "            filtered_src = []\n",
    "            filtered_trg = []\n",
    "            # max length filter,超出最大长度的句子舍弃\n",
    "            for src, trg in zip(self.src, self.trg):\n",
    "                # 过滤长度超过最大长度的句子\n",
    "                if len(src) <= max_length and len(trg) <= max_length:\n",
    "                    filtered_src.append(src.strip())  # 去掉句子前后的空格\n",
    "                    filtered_trg.append(trg.strip())\n",
    "            filtered_src = np.array(filtered_src)\n",
    "            filtered_trg = np.array(filtered_trg)\n",
    "            #allow_pickle=True允许保存对象数组，将过滤后的数据保存为 NumPy 数组，存储在缓存文件中\n",
    "            np.save(\n",
    "                cache_path,\n",
    "                {\"src\": filtered_src, \"trg\": filtered_trg},\n",
    "                allow_pickle=True\n",
    "            )\n",
    "            print(f\"save cache to {cache_path}\")\n",
    "\n",
    "        else:\n",
    "            # allow_pickle=True允许保存对象数组\n",
    "            cache_dict = np.load(cache_path, allow_pickle=True).item()\n",
    "            print(f\"load {mode} dataset from {cache_path}\")\n",
    "            filtered_src = cache_dict[\"src\"]\n",
    "            filtered_trg = cache_dict[\"trg\"]\n",
    "\n",
    "        self.src = filtered_src\n",
    "        self.trg = filtered_trg\n",
    "\n",
    "    def __getitem__(self, index):\n",
    "        return self.src[index], self.trg[index]\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.src)\n",
    "\n",
    "# train_ds = LangPairDataset(\"train\")\n",
    "# val_ds = LangPairDataset(\"val\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "a2a0f23be5871b84",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:29.097548Z",
     "start_time": "2025-02-06T14:04:29.094294Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-06T15:33:17.260764Z",
     "iopub.status.busy": "2025-02-06T15:33:17.260616Z",
     "iopub.status.idle": "2025-02-06T15:33:17.262655Z",
     "shell.execute_reply": "2025-02-06T15:33:17.262210Z",
     "shell.execute_reply.started": "2025-02-06T15:33:17.260749Z"
    }
   },
   "outputs": [],
   "source": [
    "# print(len(train_ds))\n",
    "# print(len(val_ds))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2eaf27846b953da4",
   "metadata": {},
   "source": [
    "## Tokenizer\n",
    "\n",
    "这里有两种处理方式，分别对应着 encoder 和 decoder 的 word embedding 是否共享，这里实现共享的方案"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "df6173e2f201c301",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:29.169362Z",
     "start_time": "2025-02-06T14:04:29.138854Z"
    },
    "execution": {
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     "shell.execute_reply": "2025-02-06T15:33:17.284454Z",
     "shell.execute_reply.started": "2025-02-06T15:33:17.263257Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 18107/18107 [00:00<00:00, 1236164.89it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "vocab_size: 18111\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "word2idx = {\n",
    "    \"[PAD]\": 0,  # 填充 token\n",
    "    \"[BOS]\": 1,  # begin of sentence\n",
    "    \"[UNK]\": 2,  # 未知 token\n",
    "    \"[EOS]\": 3,  # end of sentence\n",
    "}\n",
    "\n",
    "idx2word = {value: key for key, value in word2idx.items()}\n",
    "index = len(idx2word)\n",
    "threshold = 1  # 出现次数低于此的token舍弃\n",
    "\n",
    "with open(\"wmt16/vocab\", \"r\", encoding=\"utf8\") as fd:\n",
    "    for line in tqdm(fd.readlines()):\n",
    "        token, counts = line.strip().split()\n",
    "        if int(counts) >= threshold:\n",
    "            word2idx[token] = index\n",
    "            idx2word[index] = token\n",
    "            index += 1\n",
    "\n",
    "vocab_size = len(word2idx)\n",
    "print(\"vocab_size: {}\".format(vocab_size))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "461056213fba3ed4",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:29.226412Z",
     "start_time": "2025-02-06T14:04:29.217164Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-06T15:33:17.285585Z",
     "iopub.status.busy": "2025-02-06T15:33:17.285335Z",
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     "shell.execute_reply": "2025-02-06T15:33:17.292165Z",
     "shell.execute_reply.started": "2025-02-06T15:33:17.285568Z"
    }
   },
   "outputs": [],
   "source": [
    "class Tokenizer:\n",
    "    def __init__(self, word2idx, idx2word, max_length=128,\n",
    "                 pad_idx=0, bos_idx=1, eos_idx=3, unk_idx=2):\n",
    "        self.word2idx = word2idx\n",
    "        self.idx2word = idx2word\n",
    "        self.max_length = max_length\n",
    "        self.pad_idx = pad_idx\n",
    "        self.bos_idx = bos_idx\n",
    "        self.eos_idx = eos_idx\n",
    "        self.unk_idx = unk_idx\n",
    "\n",
    "    def encode(self, text_list, padding_first=False, add_bos=True, add_eos=True,\n",
    "               return_mask=False):\n",
    "        # 如果padding_first == True，则padding加载前面，否则加载后面\n",
    "        # return_mask: 是否返回mask(掩码），mask用于指示哪些是padding的，哪些是真实的token \n",
    "        # 若启动bos_eos，则在句首和句尾分别添加bos和eos，则 add_bos=True, add_eos=True即都为1\n",
    "        max_length = min(self.max_length, add_bos + add_eos + max([len(text) for text in text_list]))\n",
    "        indices_list = []\n",
    "        for text in text_list:\n",
    "            # 如果词表中没有这个词，就用unk_idx代替，indices是一个list,里面是每个词的index,也就是一个样本的index\n",
    "            indices = [self.word2idx.get(word, self.unk_idx) for word in text[:max_length - add_eos - add_bos]]\n",
    "            if add_bos:\n",
    "                indices = [self.bos_idx] + indices\n",
    "            if add_eos:\n",
    "                indices = indices + [self.eos_idx]\n",
    "            if padding_first:  # padding加载前面，超参可以调\n",
    "                indices = [self.pad_idx] * (max_length - len(indices)) + indices\n",
    "            else:  # padding加载后面\n",
    "                indices = indices + [self.pad_idx] * (max_length - len(indices))\n",
    "            indices_list.append(indices)\n",
    "        input_ids = torch.tensor(indices_list)  # 转换为tensor\n",
    "        # mask是一个和input_ids一样大小的tensor，0代表token，1代表padding，mask用于去除padding的影响\n",
    "        masks = (input_ids == self.pad_idx).to(dtype=torch.float64)\n",
    "        return input_ids if not return_mask else (input_ids, masks)\n",
    "\n",
    "    def decode(self, indices_list, remove_bos=True, remove_eos=True, remove_pad=True, split=False):\n",
    "        text_list = []\n",
    "        for indices in indices_list:\n",
    "            text = []\n",
    "            for index in indices:\n",
    "                # 如果词表中没有这个词，就用unk_idx代替\n",
    "                word = self.idx2word.get(index, \"[UNK]\")\n",
    "                if remove_bos and word == \"[BOS]\":\n",
    "                    continue\n",
    "                if remove_eos and word == \"[EOS]\":  # 如果到达eos，就结束\n",
    "                    break\n",
    "                if remove_pad and word == \"[PAD]\":  # 如果到达pad，就结束\n",
    "                    break\n",
    "                text.append(word)  # 单词添加到列表中\n",
    "            # 把列表中的单词拼接，变为一个句子\n",
    "            text_list.append(\" \".join(text) if not split else text)\n",
    "        return text_list\n",
    "\n",
    "\n",
    "tokenizer = Tokenizer(word2idx=word2idx, idx2word=idx2word)\n",
    "\n",
    "# tokenizer.encode([[\"hello\"], [\"hello\", \"world\"]], add_bos=True, add_eos=False)\n",
    "# raw_text = [\"hello world\".split(), \"tokenize text datas with batch\".split(), \"this is a test\".split()]\n",
    "# indices = tokenizer.encode(raw_text, padding_first=False, add_bos=True, add_eos=True)\n",
    "# decode_text = tokenizer.decode(indices.tolist(), remove_bos=False, remove_eos=False, remove_pad=False)\n",
    "# print(\"raw text\")\n",
    "# for raw in raw_text:\n",
    "#     print(raw)\n",
    "# print(\"indices\")\n",
    "# for index in indices:\n",
    "#     print(index)\n",
    "# print(\"decode text\")\n",
    "# for decode in decode_text:\n",
    "#     print(decode)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "628dded826e86608",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:29.254457Z",
     "start_time": "2025-02-06T14:04:29.250423Z"
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     "iopub.status.busy": "2025-02-06T15:33:17.294027Z",
     "iopub.status.idle": "2025-02-06T15:33:17.296179Z",
     "shell.execute_reply": "2025-02-06T15:33:17.295735Z",
     "shell.execute_reply.started": "2025-02-06T15:33:17.294203Z"
    }
   },
   "outputs": [],
   "source": [
    "# for i,j in train_ds:\n",
    "#     print(len(i))\n",
    "#     print(len(j))\n",
    "#     break"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d43df9ce1d4860bb",
   "metadata": {},
   "source": [
    "## Transformer Batch Sampler\n",
    "\n",
    "> Sentence pairs were batched together by approximate sequence length. Each training batch contained a set of sentence pairs containing approximately 25000 source tokens and 25000 target tokens\n",
    "句子按照序列长度差不多的分到一个批次。 每个训练批次包含一组句子对，其中包含大约 25000 个源标记和 25000 个目标标记"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "7c072c0e714bd8ea",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:29.298858Z",
     "start_time": "2025-02-06T14:04:29.288864Z"
    },
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     "iopub.status.busy": "2025-02-06T15:33:17.296652Z",
     "iopub.status.idle": "2025-02-06T15:33:17.301877Z",
     "shell.execute_reply": "2025-02-06T15:33:17.301498Z",
     "shell.execute_reply.started": "2025-02-06T15:33:17.296785Z"
    }
   },
   "outputs": [],
   "source": [
    "# 下面的info对象\n",
    "class SampleInfo:\n",
    "    def __init__(self, i, lens):\n",
    "        \"\"\"\n",
    "        记录文本对的序号和长度信息\n",
    "        输入：\n",
    "            - i (int): 文本对的序号。\n",
    "            - lens (list): 文本对源语言和目标语言的长度\n",
    "        \"\"\"\n",
    "        self.i = i\n",
    "        # 加一是考虑填补在文本前后的特殊词元，lens[0]和lens[1]分别表示源语言和目标语言的长度\n",
    "        self.max_len = max(lens[0], lens[1]) + 1\n",
    "        self.src_len = lens[0] + 1  # 加一是考虑填补在文本前后的特殊词元\n",
    "        self.trg_len = lens[1] + 1  # 加一是考虑填补在文本前后的特殊词元\n",
    "\n",
    "\n",
    "# 一个批量生成器，根据词元数目的限制来控制批量的大小。\n",
    "# 它会根据传入的样本信息，在不超过设定大小的情况下，逐步构建批量。\n",
    "class TokenBatchCreator:\n",
    "    def __init__(self, batch_size):\n",
    "        \"\"\"\n",
    "        参数:\n",
    "        batch_size (int): 用于限制批量的大小。\n",
    "        功能:\n",
    "        初始化了一个空的批量列表 _batch。\n",
    "        设定了初始的最大长度为 -1。\n",
    "        存储了传入的 batch_size。\n",
    "        \"\"\"\n",
    "        self._batch = []  # 这个就是之前的batch_size，就是一个batch内有多少个样本\n",
    "        self.max_len = -1\n",
    "        self.batch_size = batch_size  # 限制批量的大小,假设是4096\n",
    "\n",
    "    def append(self, info: SampleInfo):\n",
    "        \"\"\"\n",
    "        参数:\n",
    "        info (SampleInfo): 文本对的信息。\n",
    "        功能:\n",
    "        接收一个 SampleInfo 对象，并根据其最大长度信息更新当前批量的最大长度。\n",
    "        如果将新的样本加入批量后超过了批量大小限制，它会返回已有的批量并将新的样本加入新的批量。\n",
    "        否则，它会更新最大长度并将样本添加到当前批量中。\n",
    "        \"\"\"\n",
    "        # 更新当前批量的最大长度\n",
    "        cur_len = info.max_len  # 当前样本的长度\n",
    "        max_len = max(self.max_len, cur_len)  # 每来一个样本，更新当前批次的最大长度\n",
    "\n",
    "        # 如果新的样本加入批量后超过大小限制，则将已有的批量返回，新的样本加入新的批量\n",
    "        # 同一批样本计算时，短的样本向最长的样本对齐，所以用max_len来计算\n",
    "        if max_len * (len(self._batch) + 1) > self.batch_size:\n",
    "            # result_batch保存原有的_batch，_batch清空\n",
    "            self._batch, result_batch = [], self._batch\n",
    "            self._batch.append(info)  #箱子里的第一条样本，放入\n",
    "            # 因为是当前batch的第一个样本，所以它的长度就是当前长度\n",
    "            self.max_len = cur_len\n",
    "            return result_batch\n",
    "        else:\n",
    "            self.max_len = max_len\n",
    "            self._batch.append(info)\n",
    "            return None\n",
    "\n",
    "    # 将类的方法转换为属性，从而实现对类属性的访问、修改和删除的控制\n",
    "    @property\n",
    "    def batch(self):\n",
    "        return self._batch"
   ]
  },
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   "id": "c1dd026c49a90114",
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   "source": [
    "from torch.utils.data import BatchSampler\n",
    "import numpy as np\n",
    "\n",
    "\n",
    "class TransformerBatchSampler(BatchSampler):\n",
    "    def __init__(self, dataset, batch_size, shuffle_batch=False,\n",
    "                 clip_last_batch=False, seed=0):\n",
    "        \"\"\"\n",
    "        批量采样器\n",
    "        输入:\n",
    "            - dataset: 数据集\n",
    "            - batch_size: 批量大小\n",
    "            - shuffle_batch: 是否对生成的批量进行洗牌\n",
    "            - clip_last_batch: 是否裁剪最后剩下的数据\n",
    "            - seed: 随机数种子\n",
    "        \"\"\"\n",
    "        self._dataset = dataset\n",
    "        self._batch_size = batch_size\n",
    "        self._shuffle_batch = shuffle_batch\n",
    "        self._clip_last_batch = clip_last_batch\n",
    "        self._seed = seed\n",
    "        self._random = np.random\n",
    "        self._random.seed(seed)\n",
    "\n",
    "        self._sample_infos = []\n",
    "        # 根据数据集中的每个样本，创建了对应的 SampleInfo 对象，包含了样本的索引和长度信息\n",
    "        for i, data in enumerate(self._dataset):\n",
    "            lens = [len(data[0]), len(data[1])]  #输入和输出的长度计算放到lens中\n",
    "            self._sample_infos.append(SampleInfo(i, lens))\n",
    "\n",
    "    def __iter__(self):\n",
    "        \"\"\"\n",
    "        对数据集中的样本进行排序，排序规则是先按源语言长度排序，如果相同则按目标语言长度排序。\n",
    "        使用 TokenBatchCreator 逐步组装批量数据，当满足批量大小时返回一个批量的样本信息。\n",
    "        如果不裁剪最后一个批次的数据且存在剩余样本，则将这些样本组成最后一个批次。\n",
    "        如果需要对批量进行洗牌，则对批次进行洗牌操作。\n",
    "        通过迭代器，抛出每个批量的样本在数据集中的索引。\n",
    "        \"\"\"\n",
    "        # 排序，如果源语言长度相同则按照目标语言的长度排列\n",
    "        infos = sorted(self._sample_infos,\n",
    "                       key=lambda x: (x.src_len, x.trg_len))\n",
    "        # 组装批量，所有的batch都放入batch_infos\n",
    "        batch_infos = []\n",
    "        # 批量生成器\n",
    "        batch_creator = TokenBatchCreator(self._batch_size)\n",
    "        for info in infos:\n",
    "            batch = batch_creator.append(info)\n",
    "            # 存够一个batch的样本信息后，会把这个batch返回，否则返回为None\n",
    "            if batch is not None:\n",
    "                batch_infos.append(batch)\n",
    "\n",
    "        # 是否抛弃最后批量的文本对\n",
    "        # 如果最后一个batch的样本数目不足一个batch，则将其剩余的样本作为最后一个batch\n",
    "        if not self._clip_last_batch and len(batch_creator.batch) != 0:\n",
    "            batch_infos.append(batch_creator.batch)  # 最后一个batch\n",
    "\n",
    "        # 打乱batch\n",
    "        # 这里的shuffle是对batch_infos进行shuffle，而不是对batch_infos中的样本进行shuffle\n",
    "        if self._shuffle_batch:\n",
    "            self._random.shuffle(batch_infos)\n",
    "\n",
    "        self.batch_number = len(batch_infos)\n",
    "\n",
    "        # 抛出一个批量的文本对在数据集中的序号\n",
    "        for batch in batch_infos:\n",
    "            # info.i 是文本对的索引\n",
    "            batch_indices = [info.i for info in batch]\n",
    "            yield batch_indices\n",
    "\n",
    "    def __len__(self):\n",
    "        \"\"\"\n",
    "        返回批量的数量\n",
    "        \"\"\"\n",
    "        if hasattr(self, \"batch_number\"):\n",
    "            return self.batch_number\n",
    "        # 计算批量的数量,没有用到下面的情况，不用看\n",
    "        batch_number = (len(self._dataset) +\n",
    "                        self._batch_size) // self._batch_size\n",
    "        return batch_number\n"
   ]
  },
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   "cell_type": "code",
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   "id": "71ae51fb9433db98",
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   "outputs": [],
   "source": [
    "# sampler = TransformerBatchSampler(train_ds, batch_size=4096, shuffle_batch=True)\n",
    "# \n",
    "# #为什么这里每个批量的样本对数目不一样呢？长度*batch_number>4096的时候，就会返回上一个batch，然后新的样本加入新的batch,具体要看TokenBatchCreator的40行\n",
    "# for idx, batch in enumerate(sampler):\n",
    "#     print(\"第{}批量的数据中含有文本对是：{}，数量为：{}\".format(idx, batch, len(batch)))\n",
    "#     if idx >= 3:\n",
    "#         break\n",
    "# \n",
    "# print(\"-\"*50)\n",
    "# print(len(sampler))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "98b1add788035b2a",
   "metadata": {},
   "source": [
    "## DataLoader"
   ]
  },
  {
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   "id": "24e99727ef49605",
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   "source": [
    "def collate_fn(batch, tokenizer):\n",
    "    # 取batch内第0列进行分词，赋给src_words\n",
    "    src_words = [pair[0].split() for pair in batch]\n",
    "    # 取batch内第1列进行分词，赋给trg_words\n",
    "    trg_words = [pair[1].split() for pair in batch]\n",
    "\n",
    "    # paddings 在前在后影响不大，但在后好理解，所以padding_first=False\n",
    "    # [BOS] src [EOS] [PAD] \n",
    "    encoder_inputs, encoder_inputs_mask = tokenizer.encode(\n",
    "        src_words, padding_first=False, add_bos=True, add_eos=True, return_mask=True\n",
    "    )\n",
    "\n",
    "    # 目标语言 [BOS] trg [PAD]\n",
    "    decoder_inputs = tokenizer.encode(\n",
    "        trg_words, padding_first=False, add_bos=True, add_eos=False, return_mask=False\n",
    "    )\n",
    "\n",
    "    # 用于后续计算loss\n",
    "    # trg [EOS] [PAD]\n",
    "    decoder_labels, decoder_labels_mask = tokenizer.encode(\n",
    "        trg_words, padding_first=False, add_bos=False, add_eos=True, return_mask=True\n",
    "    )\n",
    "\n",
    "    return {\n",
    "        \"encoder_inputs\": encoder_inputs.to(device),\n",
    "        \"encoder_inputs_mask\": encoder_inputs_mask.to(device),\n",
    "        \"decoder_inputs\": decoder_inputs.to(device),\n",
    "        \"decoder_labels\": decoder_labels.to(device),\n",
    "        \"decoder_labels_mask\": decoder_labels_mask.to(device),\n",
    "    }  # 当返回的数据较多时，用dict返回比较合理\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "f53f13a987ea9201",
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   "source": [
    "from functools import partial  # 固定collate_fct的tokenizer参数\n",
    "\n",
    "# # 可以调大batch_size,来看最终的bleu，如果GPU内存不够，可以减小batch_size\n",
    "# sampler = TransformerBatchSampler(train_ds, batch_size=256, shuffle_batch=True)\n",
    "# \n",
    "# # batch_sampler 是一个自定义的批次采样器，用于控制如何从数据集中采样批次。与 batch_size 和 shuffle 不同，batch_sampler 可以更灵活地定义批次的组织方式（如按长度分组）。\n",
    "# # partial(collate_fn, tokenizer=tokenizer) 使用 functools.partial 将 tokenizer 参数固定到 collate_fn 中，生成一个新的函数。\n",
    "# sample_dl = DataLoader(train_ds, batch_sampler=sampler, collate_fn=partial(collate_fn, tokenizer=tokenizer))\n",
    "# \n",
    "# for batch in sample_dl:  #外层是拿每个batch\n",
    "#     for key, value in batch.items():  #内层是拿每个batch里面是一个字典\n",
    "#         print(key)\n",
    "#         print(\"-\" * 50)\n",
    "#         print(value)\n",
    "#         print(\"-\" * 50)\n",
    "#     break"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aa4932b257d82059",
   "metadata": {},
   "source": [
    "# 定义模型\n",
    "\n",
    "- Transformer模型由Embedding、Transformer-Block组成\n",
    "- Embedding包括：\n",
    "    - WordEmbedding\n",
    "    - PositionEmbedding\n",
    "- Transformer-Block包括：\n",
    "    - Self-Attention\n",
    "    - Cross-Attention\n",
    "    - MLP"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8d2988138abac64d",
   "metadata": {},
   "source": [
    "## Embedding"
   ]
  },
  {
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    {
     "data": {
      "image/png": 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LxYJJkybhH//4B9q3b4/WrVvjgQceQGpqKoYNGxbMoRNCCCEkSAR18nL22WfjnXfewZQpUzBz5ky0bt0as2fPxsiRI1Wde+65B8XFxRg/fjzy8/MxYMAALF26FG63O4gjJ4QQQmoHA3YDJ+i5jS699FJceumlfo9bLBbMnDkTM2fOrNV5Jsf9hugoG16454+q7G9LrlN68w0SJLxokbiF9vQVJxAAvPNZutKPjZQ8SQe9R5WOH7hX6d2/iFMq6t8HlW66Ttwxq7RcPtckS56j/2tzienccb+K82VrpbhzCtPkAxx1uEDpAx5JBNSymZzbsUecQJsrYrUTiPvKvV/6B4DmMZLHqTAyAr7wxsq92lMisUYJmovGeUSsIZqEUSlOoIJSyckEAE0d8jG1y22Gxy2uG0e5nNujuY1gyLndTi2XkpbbKMqmWZhOcBu5tRxBXoeeC0hzu/j5LdJzG3m0cVg1t5FHs8o4bPL+AmZnjzm3kZZjSXdD+cl5pLuQbNDdRpo76aS8Sn5yG0G/B/CJfm/8UVWeIn/moZq7ioJIQxprI8dSj98LD6ym3+eatw9dgj55IYQQQkIRo5ZxK4EkOG0sBD23ESGEEEJITeDKCyGEEBIEGPMSOJy8EEIIIUHAY1hNMWk1b1+Hg2lg8LERIYQQQhoUIbPyMmJrJhwRTqzMnK3Kbhk0Wul/DOmkdNRHPyh9/ZgbTf2kvS/OlKVXSY6gr450VXpmu3eVXhj1B6UPJDWVjn6WHETz956n9NxWS5Se01VyDQFAwrp8pVcUS26k4taaI0pztWyukJxH5zTZrvQPeTLu70tbKl2ZIOdzHJR8RADQIVJ2Of42qr3SRw1xJYXFyL3ZVyR9RVrkY+Yokvj4Q5otyKgQV09RiW4XApo6HUrbj2pOHbc29y6T/EQeuTwTYQ65TxbNbRRulWswHGa3kUPPBWT3nXPHa24ifdl95zCy2rQ+tfp63iHAvCTs8JfDyE+5yyLXqjuE9JxHVS05e2v4bbCmOY90quNOqhI/3z4DCmb01yaEv+GS04cXFnhrsYbgDeEPZshMXgghhJD6BGNeAoePjQghhBDSoODKCyGEEBIEah+wy8dGhBBCCDmDHIt5CfzRT23aNnT42IgQQgghDYqQWXkpeKY57A43CubIJXt+2670G69foHQLxwalLW80MfVj+/pbpSd/d43SZQckH8+Dl/+kdFTKZ0rfefYEpV0frFU653vJl9S0jbh0DomBCQCQsERyJn24r5uMt9UBGV+ktF9ztK3S50RuUfrHg4ly7oJWSpcmissncluu6dzt3OI2WhvbU+kjXnG1JEUfUXrnPsmf5NLcRvZicRUd9EqOJMMjLqTyErNdyOLQ3Ubirql0aXPvCs1d4/a9lBruEEcSTG4jKT/ZbaQ5hhy6a6cauY38fDXQ3UYezS3gPCG3kZ7zRM9t5PWT26hCS6Zks+j5k7TcRpo7qVKzSdlgdjrp7iGrxXe5jj9nj7/yqrZEr2lfNSZ0V9pJPcNby9xGoew24soLIYQQEgSOx7zU5hUIc+fORVpaGtxuN9LT07F27Vq/dc8//3xYLJaTXkOHDlV1brzxxpOODxkyJKCxVZeQWXkhhBBC6hNeWM/4Pi9vvPEGsrKyMG/ePKSnp2P27NkYPHgwNm3ahKZNm55U/+2330Z5uaxOHzx4ED169MDVV19tqjdkyBAsXLhQ/exymffrqmu48kIIIYSECE8++STGjRuHMWPGoEuXLpg3bx7Cw8OxYMECn/Xj4+ORnJysXsuWLUN4ePhJkxeXy2WqFxcXd1qvg5MXQgghJAh4DEutXwBQWFhoepWVlfk8X3l5OXJycpCZmanKrFYrMjMzkZ2dXa0xz58/HyNGjEBERISpfOXKlWjatCk6duyIW265BQcPHgzwrlSPkHls5P4oB3aLA8MGTVJlqcPkeMuFm5U+MKyL0glLfjZ3FCFb7se8J8Gx+rb1Wy8pUrqPU+rszZDb3XallCesk8DJw1eUSHkXCcQFAE9+vtKbt8oW/X/uKx+6b5OkPPuwBBFf0fJHpb0FElj784E0pW1NZRwRR+QaAKCNY5/SZQlupQ95JYiyRaSMb/uWZKUdesBukSw/5lbEygm0tAYoPmG/fZcE8OoBu6UJ0q9RrgXsOs3Bp8eJ1AJ2S+3S1m2VtobdPJ93aMuyenoAm0VLD+Dvt8iUHkDGZLPqW/pr1S3+g2b1wNxyPTDXtN2/HsgrgdEVfurrNkvbCcvP/iyY+phsWh2/6QH8Bt/6LK41+vtyJrA0oHjJgMbagK6vIeKpZcDu8YD/Fi1amMqnTZuG6dOnn1T/wIED8Hg8SEpKMpUnJSXhl19+OeX51q5di59++gnz5883lQ8ZMgRXXXUVWrduja1bt+Jvf/sbLr74YmRnZ8Nm85M/pZaEzOSFEEIIaYzs2rUL0dHR6ufTFW8yf/58dOvWDf369TOVjxgxQulu3bqhe/fuaNu2LVauXIlBgwadlrHwsREhhBASBLyGtdYvAIiOjja9/E1emjRpApvNhry8PFN5Xl4ekpOTfbY5TnFxMRYvXoyxY8ee8rratGmDJk2aYMuWLaesGyicvBBCCCFB4Phjo9q8aoLT6USfPn2wfPlyVeb1erF8+XJkZGRU2fbNN99EWVkZbrjhhlOe5/fff8fBgweRkpJSo/HVBE5eCCGEkBAhKysLL730El599VVs3LgRt9xyC4qLizFmzBgAwKhRozBlypST2s2fPx/Dhg1DQkKCqbyoqAh33303Vq9eje3bt2P58uW44oor0K5dOwwePPi0XQdjXgghhJAg4AWUYyjQ9jXl2muvxf79+zF16lTk5uaiZ8+eWLp0qQri3blzJ6xW87rGpk2b8OWXX+LTTz89qT+bzYYffvgBr776KvLz85GamoqLLroIDz744Gnd6yVkJi/lmb3hdbjR6Z+/q7LCl8TFYnxyVGnbiP1S/qY4UQCgaIhsy5/wyVY5oL3ZM3dfovSjzT9SuuU5u6TfDrItf5PvCpT+slTSEfyp5Xemc39mi1E64lfZMv+88yVK/KuWkmrgt1xxNDVvLR8ib1mp0vm5EuQVIVkD4D0q9wMAmtmLlS6Ll4/N7kppnxYu1rjsQt+LetYScfzkaW11bCUntNV+AexHxXVT6ZZ7gEpx1xgu37/S4XbdbSTvfYRVbIWG3RwZ77RojhoHfOI3PYBN36JftF1LD6CP9MT0ALqryKE5lLyGP1eRDETf7t+f46eqP5pmV5Hhs1xH37rf6sep5K/85M6qV83XuWvTzxmhqjH5O1Yfr4PUCbXfpC6wthMnTsTEiRN9Hlu5cuVJZR07doThxyIYFhaGTz75JKBx1AY+NiKEEEJIgyJkVl4IIYSQ+kRt8hMdbx+qcPJCCCGEBAEvLH43hKxu+1CFkxdCCCEkCHDlJXBC98oJIYQQ0iAJmZUX+x15sEe4YFwleX3eO2up0hdef6fSb5z1uNKjLpNyANh7sbg7wt/Rcg9puXm+/p9s9rPw4lyl70wTm9n9fWSXwqb//kHpxftk2+XpzT4wnfvzJlcqHferOFN6ugqVLmwtLpqK38Ue47L4tsq498pHoDRRoskNj9n5Em+VekcTZM67q0I8/61d4tJyHNFcOrqnpkRcTHtK9ayj4gSyl5iXQg3NVWQ7Kve/0q3VqZByq1Mbu5brJsouLqtDdslR5bbIub3OE3Ib6W4ju+6o0XIb+UndYbHrriLfbiOPtux7Um4j7Rx2LbeRvjGV1aI7mvTcS3oOI60+fLuWrCckvqlOrqLqfPPxl8PIr0Ooys5O/xL56cq5RIgvap/bKHTXH0Jm8kIIIYTUJ7yGxe8Xheq2D1VCd9pGCCGEkAYJV14IIYSQIOCt5WOj2mxw19Dh5IUQQggJAnpm6EDbhyqhe+WEEEIIaZCEzMrLOx0/RnSUFd1um6DK9njEbXTWmA1Kp9rE3VI4QtxJADC182dKv9ErU2nrQXH8tFguzpcFzfsr/fMff1b61t7iHkl4qUjp7F/7Kt02TRwxAFDZWtKLR26RfEgJ1ggZb5oEcIXvEl1mSI4ma1iY0mF50n9FW3HjWGxmC02kVfILlcZL+bYySYh0XqTkWHLK7TCdG0flHLmlktvIYjustJZGCQDgDZP3w1Iq99aj5fzS3VF2l2j9OiK13EZwSJ/hutvIcYLbSHcV+cthpJWbnFVWzQmku41MeYqglZsdXroTyaG5jSq0nEdOLbdRgSHvq+4qMruQfOc8ssG/20i/I/5cQtXJeVRdatympg4hrb61ut/fajymMxBIGcLBmo0FDyym3/NA2ocqITN5IYQQQuoTfGwUOKF75YQQQghpkHDlhRBCCAkCHtTu0Y/n1FUaLZy8EEIIIUGAj40CJ2QmLy8VtITbY8f0UYtU2RVf3qr0xvNfVnrC7guUXtRrvqmfs5wSJfro5TFKR2+T4NOEJRKYG9n+LKXLzpPgyrO7bVW6KLGJ1P9J297/QvO8uqC9BPDGv79DaT1ItKK1BMQmfCB9/e4pU9oaK+OO3CvnCGsiUbZ6UC9gDmwsT5DzbS+R9ADXxUp7Z6FERR7xSsCuUSrj2H9UIn9ddglatpeYTg2PWz6mzoKjWrnU0QN2nU65zxarfKuJtMm9gV2CXt1Wqa+nAAAAq/atyOs7wwK8dt8Ro1Y/6QEcNhlruSkFgDk9gB6Yqwfa6snYrFq/+h+y6gTmVpWR1m96AD9t9G31bdX4JmmYAoL9/wHW73/Nt+4P3WBG0jBgYsbACd0rJ4QQQkiDJGRWXgghhJD6hAFLlSug1WkfqnDyQgghhAQBPjYKnNC9ckIIIYQ0SLjyQgghhAQBr2HxGxxf3fahSshMXl5bOBg2lxvZd89WZS++IMeXnyNOnuz/9lB67u1fmPr5tULcLv0GS0qBLze3VTr2NUkpkPy1bOP/VlFLpW9KWaX0Y2f9WemEn8SZ84O2mz0A5LeXD2pMfr7Sv1fKfvpdW+5RunRXstI/lzdV2kgQt1HYXnHgNI+TXAG5UZGmc+uOJm+cjHFXUazS8dp2+M5CqX/IqzlGyqXtoRK556lOsfI4Ssy2Eo9bS1WguZX09AAw5HzhTu3G6ekBNLeR4dAcTNpuCV6neTHSZtHcRuaMCdKX9lvk0cZhsWnpATSrjENLA6D/8XHZxPUEwJRtVk8P4PWTNkBfQrbpDjTNtWS16O6kKtIDaOewaX8fTW30e+PXheSn3Gfp8TZVHKxvNKSxNnIsDfC98NQyq3Rt2jZ0QvfKCSGEENIgCerkZfr06bBYLKZXp06d1PHS0lJMmDABCQkJiIyMxPDhw5GXl1dFj4QQQkjD4Phjo9q8QpWgr7ycddZZ2Lt3r3p9+eWX6tjkyZPx/vvv480338SqVauwZ88eXHXVVUEcLSGEEFI3eGGt9StUCXrMi91uR3Jy8knlBQUFmD9/PhYtWoSBAwcCABYuXIjOnTtj9erVOOecc870UAkhhBBSDwj6tG3z5s1ITU1FmzZtMHLkSOzcuRMAkJOTg4qKCmRmZqq6nTp1QsuWLZGdne23v7KyMhQWFppehBBCSH3DY1hq/QpVgrrykp6ejldeeQUdO3bE3r17MWPGDPzhD3/ATz/9hNzcXDidTsTGxpraJCUlITc312+fs2bNwowZM04qT56/HnaLE+cOGqXKEr/4TukJH4xRutMbu5V+YEQfUz85h1oo/X8dFiv9RnRXpT/pcrbSxgbJYfT0r5IzaXWffyl9V29J0tP8bTn3m/n9TOc2OoqryKK5aNaVycrVwCablP40V/r96kh7pcuTxEnk2n5I6bMixam0N76v6dxFXnH5xMbLOHILJKdTlFUcQ84j4oLZ74mQa6gUt1FRkYzP4pQ8TCe5jcI0m4/mVvK4fdsLIjS3kcUuH/Fwq+ZCcmi5jXTHjuPE3EYyv/eX28iw6fmCRNtMuY0Em1XLU6S5dOwWcy6rCs3GpLuKyg3f12RyFcGfq0jPeeT/u4u/Z+n+3EPV6Ud3JwWEHzdJTceEquo3QMcKabjQKh04QZ28XHzxxUp3794d6enpaNWqFf7zn/8g7ITEgNVlypQpyMrKUj8XFhaiRYsWVbQghBBCzjxGLbNKG9xht34QGxuLDh06YMuWLUhOTkZ5eTnytf1MACAvL89njMxxXC4XoqOjTS9CCCGENB7q1eSlqKgIW7duRUpKCvr06QOHw4Hly5er45s2bcLOnTuRkZERxFESQgghtccDS61foUpQHxvddddduOyyy9CqVSvs2bMH06ZNg81mw3XXXYeYmBiMHTsWWVlZiI+PR3R0NG677TZkZGTQaUQIIaTB4zVqF7fiDeEYraBOXn7//Xdcd911OHjwIBITEzFgwACsXr0aiYmJAICnnnoKVqsVw4cPR1lZGQYPHoznnnsumEMmhBBCSJAJ6uRl8eLFVR53u92YO3cu5s6dW+tzWVs1h9XmQpPHtEDgDMlh1PGlw0pX7til9HtL+pv6cWjO67i7JLnO2JhflH5loAQiJ/0ibqOK7HgZTx+ZbRf3lnxJnjni+Plg61mmc/dvtU3pfU0Tlf6sQN7GMU1kk79P9jdXeu2BVkofTRFnjzNHrruLW5xOHyf80XTuQ17Ju9M6VhxK322WfE1hFrkfjkJxweRWSi4lw6M5e4o1+45ba1use3OA8kjt6abmNvL6cxs55NweLYdRlFXus9cp5S49388JjiKrtixr+PltMey620jGbnYVaXmfbHIPdIeQw2K+bt0Gacpt5Nc9pDt7Tu0q0utbLf6P2aqxNF1Tx09V9avTl81SjSfedfittCHlzQlorA3o+hoT3loG7NambUMndK+cEEIICSJeWGr9CoS5c+ciLS0Nbrcb6enpWLt2rd+6r7zyyklpfNxut6mOYRiYOnUqUlJSEBYWhszMTGzevDmgsVUXTl4IIYSQEOGNN95AVlYWpk2bhnXr1qFHjx4YPHgw9u3b57dNdHS0KY3Pjh07TMcfe+wxzJkzB/PmzcOaNWsQERGBwYMHo7S09LRdBycvhBBCSBAIxg67Tz75JMaNG4cxY8agS5cumDdvHsLDw7FgwQK/bSwWC5KTk9UrKSlJHTMMA7Nnz8b999+PK664At27d8drr72GPXv2YMmSJYHclmrByQshhBASBI7HvNTmVRPKy8uRk5NjSrtjtVqRmZlZZdqdoqIitGrVCi1atMAVV1yBDRs2qGPbtm1Dbm6uqc+YmBikp6dX2Wdt4eSFEEIIacCcmM+vrKzMZ70DBw7A4/GYVk6AqtPudOzYEQsWLMC7776Lf/3rX/B6vejfvz9+//13AFDtatJnXRD0rNJnik2TomENc6P9X75VZZtfkfw97cf8qLQxQFxIrd/Yb+7IkLD8J/8ibqBJ8dK+cmCB0rZPWyud8rU8/8sZL11e0eUHpX+qFFeJsSHKdOpLe6xX+sW0K5X+em+s0o8kf6G0p0hyEG3f00HpyGRZaozR6rS1i4uoNFHcPwCwu1LyE7WNPKD094fbwhfWIrnWXRXxPuvYirScRZrbyF5izvFTkij1jHJxEnndZnfOcaKdcu58LbdRhFV+oQ2HzNudWs4d7wm5jXRXi7/cRtDcRh7t82G3yfgqNDeH06o5rvTcRlb/uY2sFu8pyz3adxGbntsIFp/llYbv+oD/vSdMDiWtX3/lRh26WPy6kBqSU6YhjZWcdryoZW6j//+7dmIKnGnTpmH69Om1GZoiIyPDtDFs//790blzZ7zwwgt48MEH6+QcgRAykxdCCCGkPmHUwjF0vD0A7Nq1y5QKx+Vy+azfpEkT2Gw25OXlmcpPlXZHx+FwoFevXtiyZQsAqHZ5eXlISUkx9dmzZ89qX0tN4WMjQgghJAgczypdmxeAk/L5+Zu8OJ1O9OnTx5R2x+v1Yvny5dVOu+PxePDjjz+qiUrr1q2RnJxs6rOwsBBr1qw5ral8uPJCCCGEhAhZWVkYPXo0+vbti379+mH27NkoLi7GmDFjAACjRo1Cs2bNMGvWLADAzJkzcc4556Bdu3bIz8/H448/jh07duCmm24CcMyJNGnSJPzjH/9A+/bt0bp1azzwwANITU3FsGHDTtt1cPJCCCGEBIFg7LB77bXXYv/+/Zg6dSpyc3PRs2dPLF26VAXc7ty5E1ar9Hv48GGMGzcOubm5iIuLQ58+ffD111+jS5cuqs4999yD4uJijB8/Hvn5+RgwYACWLl160mZ2dUnITF4+PP85REVZMeL6u1XZ2+fPVnrS0NuU3nmJPINsf6v/XQJf/vwCpSMzJUh06lkfKv3EOdcpnbDkZ6UX7D9P6duaynLbfYmXSv2fzAGp57r3Kv1Yh3ClC7ZJyoPIPtqHxZD2zl2yjFiSIlGDRqVst59kk8DYkkTzL8VvFU2V7hQmKQycBVrQprZVvaVItuLfXRan9SRBqY4jWmBnmKQssBVXQqcyTMZuVMoxi0sLcNUCa6PsEpib75B7owfsep36tvwyDs8JAbtW7cmq199viyk9gGiH3Xdgrh6wqwffuqzm69YDcPX0ACVaSgGnVl6plVu196LCq5XrqRCqCBT0d8xf0Ky/wNyAgmxrEcBYHeoyiNj/Sc5QG9Kg0R/9BNo+ECZOnIiJEyf6PLZy5UrTz0899RSeeuqpKvuzWCyYOXMmZs6cGdB4AoExL4QQQghpUITMygshhBBSn6hNfqLj7UMVTl4IIYSQIBCsx0aNAT42IoQQQkiDgisvhBBCSBDgykvghMzk5bDXgQqvFd0nfa/K2mlXX/jXQqUf7LBM6f/re4mpH9uhI0qnvS/ukNlxg5TePGi+0vdmiBsk9lVJG/DZBklNMK/5l0pXtmumdPTGw6ZzJ9silS5oLx/ayO2iywxxD1nDNKfNbumnYIA4oyyawyjSKq6eo2IuAgBsKZW8FRdEiWvKlS91Srzl2g/iNvq9RNxGFptck10yE8AbrrmNSrR+AFS6JTWBUSH33OHWnEfadUQ7tDTsDtnTP9yipRZwak4eVCMFAADNGGRyVsGmb9GvpQewSrlXc5LY/aQH0B1FAFBhchXJtRYY8r7qriI9w6zNop/bd3oA85b+Zgw/x2rqQvJHTesfaxR4fWt1F5lrOq4z8Z9HCP8H1djh5CVw+NiIEEIIIQ2KkFl5IYQQQuoTXHkJHE5eCCGEkCBgoHZ251De15CTF0IIISQIcOUlcBjzQgghhJAGRcisvPz5w1tgDXNj69UvqLILN/5J6Y96ikNId/VMu140AET9FiX1XlqndELzXkofOL9E6SF9flB6R7NUpWNyxF1TcqG4YA6dJc6axMVbTefWnUToUCR9/VvcJ9sqpS9rvLh8onaJWyWm6SGpEy45knRHRlkTc16lX4vEbXRD7BqlXfmycFlkyDmMo+L42VuSqHSEUyxGDrkEeMLE5mM7LPfv2DHRhkccOW6X3A+T28imdeyQj7hbyx2ku42s2rKtV96Wk/A65Fo9Wt4oq11z9ui5jWwy1nLt3ppzG+k5lsxuI4+hj1F3CUm57iqq1HIYmVxFfpalq8xt5KeNnhfIVo3lbrNryfd3JesJ/dQ891DofvskDRuuvAROyExeCCGEkPoEJy+Bw8dGhBBCCGlQcOWFEEIICQJceQkcTl4IIYSQIGAYlsB2m9bahyp8bEQIIYSQBkXIrLx0eHY37FYXJg/oo8pKXpI8QqWPicXhzaIYpbOGfGDq5/Wd/eSHBTL3S/x8j9JPHjhX6dubrlB6fL/JSjfNEdfNZ0cTlD7cVdwj8UWaawbAr1pen3Nb/ab0nu0tlf76aBulvcnxSoftlr46xUuio1/jkpWu0NxCtqaSmwgAthVIX01ayXW78sUhs98jbhejXFxPB4+IgyrSKXYeZ5Hc88pwLY9PWZnp3JWa2wiayyfCpeVAssmYomzidDJcmttIyw/kceo5heTc3ip+IwyHbxuM7jbyaFYZp01zN2nfkFya66lcS5jk0MoBsxMp3Frms9ym5zbSXDdWi3Zvq+FCsp3wBc6UD8miubH8upB81/dnHKq5oyjINLTxNmIsjei98MJSq03qatO2oRMykxdCCCGkPsGYl8DhYyNCCCGENCi48kIIIYQEAQbsBg4nL4QQQkgQ4GOjwAmZyYtRWATDUo7sp89WZbFvfav0xX+6RemygxIh+ttlL5n6SQ+XQNm7LpigtPPDtUr/Jztd6UeGfa/03v7ylK79QzuUfmn3eUqndslT2hZpTk2wrLiL0pfFr5f2e2SL/88Pd1K6pLkWKLtWztc7UvQviVL/sFcCXZsn5JvOvXOfBOxGWrSg23zZon+3RwKdvVpw8dEjbqUtbpfSjiIJNq2IkKBSlJoDdj1hviP0olwyXotD0gtE2STY2HBIv25tK309YNeUHkC6OQkt7tWUBsCmBexWQA/Y1dMDSGO7lh7Aqz25PTE9gCkw16KnJvDdxhRkqwXy6uVW7W9dVX/4/H2j81fur6+a9nPsYM3G5D8quIb1CTnDcOUlcBjzQgghhJAGRcisvBBCCCH1CaOWj41CeeWFkxdCCCEkCBio3Z5HofwElI+NCCGEENKg4MoLIYQQEgS8sMDCHXYDImQmLzv/2hk2lxvNH1qtymytZVv91BfEBWMtF6fMskzzLbpQ26p+x+WiO3/bVOlmn0n59kuPKN01XZxKJfn5Sm/5Xhw/ky78WOmPW2eYzv3hXnH8vN7x30rPO3hY6W9+76x0WAtte/mlcr5urt+VPpoiTqU9HrnWTrHiegKAHRtTlHZYtC3t88Xxs708URpo2/hbCrR7GCY30FEkTpniFLH5GGXatv8AvGFe+CLWKecucmpuI6uUe11S7tYdO5rbyGaRBciq3EZwaA4ezc1jt+npAaS6U3MVVWgOIXN6AHmPTnQbeUxOJGljTg+gpQHwU+7vD5zZnWTxe8zkxvLrHvJZXGv09+ZMUGdbz5+B9fwajzWUnzHUU+g2Chw+NiKEEEJIgyJkVl4IIYSQ+oTXsMDCTeoCgpMXQgghJAgYRi3dRiH8KJCPjQghhBDSoODKCyGEEBIEGLAbOCEzefn7dW8gPMqGFz+7UpVtuUxy/6T9/WuprDkc/vrlKFM/j/d/U+nJf/hE6f8MuFjp6P9tVfrFQ+cqndX8U6VnxUg+o8Qc+QBeduUGpRefJX0CwN6t0Uond5G8R0al5BfybJNrKm4ubb1l4sBpYddcPsnyEdhcnqR014jdpnN/frg3fGEpLFF6e2kTn3UcR+R+GhF6biNx0FSGS74ko9zsNkKY1NPfmxin5DAqcsi90XMbeZ1S3+3HVWRFdd1Gen4h0Q7tflZozhw9t5G/fEQVht1nOQCUeOWe6LmKdFeRFbrTSc9hpLmNTK4izZ3k9b/w6j8nkWjdhVRneYcC6ctvPzWrHxB0/JBawMlL4ITM5IUQQgipTzBgN3DqTczLI488AovFgkmTJqmy0tJSTJgwAQkJCYiMjMTw4cORl5fnvxNCCCGEVMncuXORlpYGt9uN9PR0rF271m/dl156CX/4wx8QFxeHuLg4ZGZmnlT/xhtvhMViMb2GDBlyWq+hXkxevvnmG7zwwgvo3r27qXzy5Ml4//338eabb2LVqlXYs2cPrrrqqiCNkhBCCKk7jruNavOqKW+88QaysrIwbdo0rFu3Dj169MDgwYOxb98+n/VXrlyJ6667Dp9//jmys7PRokULXHTRRdi92xxaMGTIEOzdu1e9/v3vf/vsr64I+uSlqKgII0eOxEsvvYS4uDhVXlBQgPnz5+PJJ5/EwIED0adPHyxcuBBff/01Vq9eXUWPhBBCSP3n2ATEUotXzc/55JNPYty4cRgzZgy6dOmCefPmITw8HAsWLPBZ//XXX8ett96Knj17olOnTnj55Zfh9XqxfPlyUz2Xy4Xk5GT10v8/Px0EffIyYcIEDB06FJmZmabynJwcVFRUmMo7deqEli1bIjs7229/ZWVlKCwsNL0IIYSQxsqJ/+eVlZX5rFdeXo6cnBzT/6tWqxWZmZlV/r+qU1JSgoqKCsTHx5vKV65ciaZNm6Jjx4645ZZbcPDgwcAvqBoENWB38eLFWLduHb755puTjuXm5sLpdCI2NtZUnpSUhNzcXL99zpo1CzNmzDip/KLwg4gOt2L6veJcmdL5v0q/8V/tzTwoE560Reb53X2O4Ur//MeXlX5qkEyBw/+7X+nF6/sq/Y+L1ivt7dRK6fjvJDdRmj1K6YNdzOeO/FV+LjPEYWTV8gVFbZcArsL+4rrRXTpxVqlfLCmL8NNRsSedF/mL6dxu7XN41NB+MYqLldxRkiCns+Ur7dDmj94wLYdUsbiKKsIlx5JRobmLADg0t5HFJk6bWIdc326nOJ2iLHpuIy13kDZX13Mb6ZzoNtJzGEHLYVShleuuIq8pt5GWjwgyDj23kZ6nyGkxX3eBoeWBsujOJT0vk+ZC8vrJeaS5ivRPlO5UOPFbjP8cRjULEAzIDVELB4+1Ot/HAhrTGQiMDOHgy1ClrtxGLVq0MJVPmzYN06dPP6n+gQMH4PF4kJSUZCpPSkrCL7/8clJ9X9x7771ITU01TYCGDBmCq666Cq1bt8bWrVvxt7/9DRdffDGys7Nh0/5m1yVBm7zs2rULd9xxB5YtWwa3211n/U6ZMgVZWVnq58LCwpPeWEIIISTYGKide/542127diE6WraLcLlcvhvUkkceeQSLFy/GypUrTf9vjxgxQulu3bqhe/fuaNu2LVauXIlBgwadlrEE7bFRTk4O9u3bh969e8Nut8Nut2PVqlWYM2cO7HY7kpKSUF5ejnwt+zIA5OXlITk52W+/LpcL0dHRphchhBDSWDnx/zx/k5cmTZrAZrOd5No91f+rAPDEE0/gkUcewaeffnqSueZE2rRpgyZNmmDLli01u5AaELTJy6BBg/Djjz9i/fr16tW3b1+MHDlSaYfDYQoK2rRpE3bu3ImMjIxgDZsQQgipE2oXrFvzR05OpxN9+vQx/b96PPi2qv9XH3vsMTz44INYunQp+vbt67fecX7//XccPHgQKSkpp6wbKEF7bBQVFYWuXbuayiIiIpCQkKDKx44di6ysLMTHxyM6Ohq33XYbMjIycM455wRjyIQQQkjdUVfPjWpAVlYWRo8ejb59+6Jfv36YPXs2iouLMWbMGADAqFGj0KxZM8yaNQsA8Oijj2Lq1KlYtGgR0tLSVMxpZGQkIiMjUVRUhBkzZmD48OFITk7G1q1bcc8996Bdu3YYPHhwLS6uaur1DrtPPfUUrFYrhg8fjrKyMgwePBjPPfdcQH1duP462MJdyD5b7GCRVnlmN/Ovsq1+5OYYpVNnm4OJE+P7KL17gASMXpOxRukf07Rg3K9l+a4gUwJJ9/WR7f2TFn6ntB4MazvL7JSKe1WCWn/VglqtiRIoG/ObBPIm/kkCh23REgisBzWWJksg6M9HZJY8Kk6uBwDch+S35JBXAm29xZIeYKfWPtop5c4j0o8nUiJinb9LsG+lXBoMj3mb/DC3nE8P2I22FUklh3yUI6xS3+PSt+XX0gDIzvsmvA7zXwOPIQGxVodor/ZXw2mX96Jcu7dOq54GQAsctvhOG2A94S+RVztWncBcL/wEIfv5duavPmDeP8JWjTQA5uBf3wu65nQCfk9dBWciaPb0n4JUD0sovBe1DNgNJMj72muvxf79+zF16lTk5uaiZ8+eWLp0qQri3blzJ6xW+R1+/vnnUV5ejj/96U+mfo4HBdtsNvzwww949dVXkZ+fj9TUVFx00UV48MEHT1vsDVDPJi8rV640/ex2uzF37lzMnTs3OAMihBBCGhkTJ07ExIkTfR478f/h7du3V9lXWFgYPvnkkyrrnA7q1eSFEEIICRUC3SVXbx+qcPJCCCGEBAFmlQ6coO+wSwghhBBSE7jyQgghhAQDw1K7nZVDeOUlZCYvTWa7YLe78fkrko/hqyPtlX71wpeUXtDjD0rve12cPAAQ9+lmpe+8VTJcP5/2jtKXXiB++abZh5T+d2EHpQ/31lxBz4lr6dsyic6+rM1PpnP/sFnaf1IkNvPK5rI1ftiOAqXPTtgu/SZKW93RFJYsjp8th+Rak9LMHw33QXHU7KkUl5ZRLtdxsFAcWzFhUsdZKA9mK6KkX+dRcV9VSlNAc/gAQJRbxmvRXEXxdnEbGW5xMbl1N4/TtwvGc0IagON4nSc4fjT7ic2upQfQHjY7TK4iOYeeBqDcsGv1facHCLea85GYHErQrklz3VgtehqAU7uQbNrfOt2FZLOY/whW5UTy1cbfo3e/z+SreFZf46Vwf/VDOB6ANAwY8xI4fGxECCGEkAZFyKy8EEIIIfWKIGxS11jg5IUQQggJAnQbBQ4fGxFCCCGkQcGVF0IIISRYhPCjn9oQMpMXy+oNsFgcuOdfo1WZU0sdNOPOb5Xu1XyZ0n/402RTP0nPSc6fTR+lK93kNknOc2iQuGgSXv9N6Wd/Pl/p87tuUnpfiqQif+uQJN0Z1eQr07l/+F3yE3209yylj7YJUzrug+1KZ0SKM+rr1LOVzvNI7p8OiZL/6PstLZTW8z4BgOuQOGG2V4i7yagUt1FZvrSxhMmYXIXilCmPlMW+iFLp0xNudhjpxLrEjeVxik0oxib5k7wuKQ/X8gB5XLrbSLS/3EaG/US3kfRlt+uOH6nntvt2D7n8uIrcFt/lTos5p1OF7h7ym9tIy7ek5xfSVpMrvVqOpGrkKarqmL88SdXJeeQPm6WaC8B19Ee+oeXMqfF4G9j1hTJ8bBQ4ITN5IYQQQuoVDNgNGMa8EEIIIaRBwZUXQgghJChY/v+rNu1DE05eCCGEkGDAx0YBw8dGhBBCCGlQBLTyUlxcjEceeQTLly/Hvn374PWanSK//fabn5bB48jVfWFzutF6zkZVZtFcDtdfeYnS/2z1ttJpV2819XN0rbh8Wn4geYv+OyZO6azenyn9nlecRPZscQvdNGGV0lO7jlN66dYkpZ9IyTad23PkiNLbt3ZWOixNlg6jC8RC1cVxUOni5uIE2lwhY+0Tu1PpDfvawh+2fHH2bClL8lnHni8uGISL28hRKO6aomQtL1KZuI2MCLPTRifeJec+4JDcT9E2cXV53bqbR+6H7jbSXS1+3UZO82fZo+cwsus5jKSO26bnMNLcRjbdVaTlNrKcuvzYMd+5ivT8SXp5pZ9yfw4hj+ZCsp6w/GxyLpkcSj67qvE3wCpdEvXx22RNr7s+XgOpf3DlJWACmrzcdNNNWLVqFf785z8jJSUFFkvoPncjhBBCAoJZpQMmoMnLxx9/jA8//BDnnntuXY+HEEIIIaRKApq8xMXFIT4+vq7HQgghhIQMhlHFo9hqtg9VAgrYffDBBzF16lSUlJScujIhhBBCTsaog1eIEtDKyz//+U9s3boVSUlJSEtLg8PhMB1ft25dnQyOEEIIIeREApq8DBs2rI6HcfrpesuPcEY6sWtlrCozioqV/v3lDkr/+fpRSi8561+mfs697C6lW93/tdIzNwxVem2/hUp/0DFD6ZSv5Xz9ssTVsq+3WF9sP2n6D+aFMYtdJolRv8pbd+QsyS8EQ/pNsYnj50hzCez6/mgrpXuEi9sobJ90U2ZofQJAYZGSvxSJg8pik3JXvpY7KEpcRfYj4iqqiNDcRuWSY8kWLuez2DTXEoB4p9y3Ay5xbEVZJeeR1yVtHJqryJzbSCvX3EZ6/iKL48TcRvKzy5TDSPp1WjUXEjS3kUWuSXchObQcRiVel8/yY+fQ28i59dxGVi3xjdkhJJhdRfBZ/0T8LUfXOJdKXQYUamPS30v/DqgAzh3CAZAkCDBgN2ACmrxMmzatrsdBCCGEhBQWo3aJQhtaktG6pFY77Obk5GDjxmP7ppx11lno1atXnQyKEEIIafRwn5eACWjysm/fPowYMQIrV65EbGwsACA/Px8XXHABFi9ejMTExLocIyGEEEKIIiC30W233YYjR45gw4YNOHToEA4dOoSffvoJhYWFuP322+t6jIQQQkjj43jMS21eIUpAKy9Lly7FZ599hs6dZYv6Ll26YO7cubjooovqbHB1yexmaxEdZUO7O29WZdFb5Y1PelkcUget8vjLM9O8LjdwsNTb8Xyq0vYVMdLmbGmzP6OJ0omLf1BaDwSt6CNBr03/LUG2v1ZIQCoA2JJkRStuswRwJlwskba2KAlodVjk7S1pLkGpOQUSsHtF1PdKhx2QMR32SpAtABhHZIzbjsg4IpwSdOvMl/qVURKI6txTIOWRcp+8FXIN4WHSjx6YDAAJDmkPl/QbZZUxerT0AA49MFeqm/A65Vo9WpCz1X5C0KwWzOvUjpVr53DbJDBXD7J1Wf2lAfD4rG+zmFMT6IG5pu3+te36TekBvL6/i3j9ZJ7VA11tJ9TxF5hrmIKCrX7Ka5tOoBH/QQ7h/2yID/jYKGACWnnxer0n2aMBwOFwnJTniBBCCCGkLglo8jJw4EDccccd2LNnjyrbvXs3Jk+ejEGDBtXZ4AghhJBGCzepC5iAJi/PPvssCgsLkZaWhrZt26Jt27Zo3bo1CgsL8cwzz9T1GAkhhJDGBycvARNQzEuLFi2wbt06fPbZZ/jll18AAJ07d0ZmZmadDo4QQggh5EQC3ufFYrHgwgsvxIUXXliX4yGEEEJCA+6wGzDVnrzMmTMH48ePh9vtxpw5c6qsWx/t0vfu7QPnEQf+faU81lp4YIDSO5ZIluwm72xUeszoq039vNbuLaWHXCypAlJWHFB6/s2dlD7YX1w08S+LYye7TPanv7rDd0qv2yht3z3Sw3TuijZJSkdsPqx0etJmpdckiwPsqCFunLDmR5TeeKCpjLuVfATC9ok7Zlel2abjPSrOp7zD0Uq3DctX2p2vOaiipV/nFmlbEal1qrl8YsKljsVh/ljG2+W+GW4JFI/Qtt+v1NIAOCzi0vGcHFcOwOw20p1fdueJW/TLMbdddxXJE1fdVVSuu4pMbiMZU7jmkjKlAMCJ59a29de20vTnQqrU6tu0v2l6GgCbRUvhUIWrx/DTxq95qIauohqnGQD8/6Gu6dJ5CC+110dCeZdY7rAbONWevDz11FMYOXIk3G43nnrqKb/1LBZLvZy8EEIIIaRxUO2A3W3btiEhIUFpf6/ffvvttA2WEEIIaTQEKWB37ty5SEtLg9vtRnp6OtauXVtl/TfffBOdOnWC2+1Gt27d8NFHH5kvwzAwdepUpKSkICwsDJmZmdi8ebOf3uqGgNxGM2fORElJyUnlR48excyZM2s9KEIIIYTUPW+88QaysrIwbdo0rFu3Dj169MDgwYOxb98+n/W//vprXHfddRg7diy+++47DBs2DMOGDcNPP/2k6jz22GOYM2cO5s2bhzVr1iAiIgKDBw9GaWnpabuOgCYvM2bMQFFR0UnlJSUlmDFjRq0HRQghhDR2LJC4l4BeAZzzySefxLhx4zBmzBh06dIF8+bNQ3h4OBYsWOCz/tNPP40hQ4bg7rvvRufOnfHggw+id+/eePbZZwEcW3WZPXs27r//flxxxRXo3r07XnvtNezZswdLliwJ+N6cioAmL4ZhwGI5+bZ9//33iI+P99GCEEIIIaeDwsJC06usrMxnvfLycuTk5Ji2NbFarcjMzER2drbPNtnZ2SdtgzJ48GBVf9u2bcjNzTXViYmJQXp6ut8+64IaWaXj4uJgsVhgsVjQoUMH0wTG4/GgqKgIN998cxU9BI9v5/WEzenGlIc+V2XPNpMb23XsBKVbPS7un9z/dDH14/qbOD0qLhPHj2f+FqWf/nag0n/qJbmQNqRJTqH5ebFK/z1Vnh9+u0PsOG/tkBxLAGBp71a6yXo53wWR4o5a1aq/0r9XijumR7Lshpz9Y3ulI63Sp3u/OH5+KU8xndvwaPl4DkobS3i40q4CqVMaq7lrNKdSZaTZUXOcBLc8hix3mZ1O8fZipb1hYh8K13IBVYbJPNzkNpKhmjAcuttI+rGfkNuoXHcb2Xy7h8Js5T7L3RapX2rIuGMtcq0VXv+5jbyae8ipOZEqTS4kvb6e80j73fST86gqx4/He+rcRtXtS43JUs3vSnXkoGhoTowaj7eBXR/xQR1ZpVu0aGEqnjZtGqZPn35S9QMHDsDj8SApKclUnpSUpPZsO5Hc3Fyf9XNzc9Xx42X+6pwOajR5mT17NgzDwF/+8hfMmDEDMTGSZM/pdCItLQ0ZGRl1PkhCCCGk0VFHiRl37dqF6GjZwsLl8pORthFRo8nL6NGjAQCtW7dG//79fSZnJIQQQsiZIzo62jR58UeTJk1gs9mQl5dnKs/Ly0NycrLPNsnJyVXWP/5vXl4eUlJSTHV69uxZk8uoEdWOeSksLFS6V69eOHr06EnP2Y6/CCGEEHIKzrBV2ul0ok+fPli+fLkq83q9WL58ud+nJhkZGab6ALBs2TJVv3Xr1khOTjbVKSwsxJo1a07rk5hqr7zExcVh7969aNq0KWJjY30G7B4P5PV4fMc1EEIIIeQYwdhhNysrC6NHj0bfvn3Rr18/zJ49G8XFxRgzZgwAYNSoUWjWrBlmzZoFALjjjjvwxz/+Ef/85z8xdOhQLF68GN9++y1efPHFY2OwWDBp0iT84x//QPv27dG6dWs88MADSE1NxbBhwwK/uFNQ7cnLihUrlJPo888/P0VtQgghhNQ3rr32Wuzfvx9Tp05Fbm4uevbsiaVLl6qA2507d8JqlYcy/fv3x6JFi3D//ffjb3/7G9q3b48lS5aga9euqs4999yD4uJijB8/Hvn5+RgwYACWLl0Kt9uPY6IOqPbk5Y9//KNP3VCIXvwt7BYH+g+S1AWP939T6b9e+7HSb/10kdIp/xVXDwA8PL6f0s90W6z0rOjzlE5cIXmLbr3gC6VHpksupI0/SGR2p1arlNZzCOVvSDCd29JBVrvijkiuoi5OceMUpsm515c1U/rcWLmOdbmS/0jHelj27tlQ0sxnHQBwHhKHjBETIeWHxXVT2FJcSMZRbaOiSHHgQHOfJLrl3Ltd5me3sTa5Po9bPrJu3e2mxadZtaehXrkdZlzi7PFojiKn44T8Qpprx23XcxjJPdBzG5UaTq1c3F4l2kAcmgupTHMhVTe3kdlVpOc8OrWryGpyIfkuP7GN+YDvYn+OiRr3UwV+8yfVJTXM0UTHD6kVdRSwW1MmTpyIiRMn+jy2cuXKk8quvvpqXH311SdX/v9YLBbMnDnzjG5SG9A+L0uXLsWXX36pfp47dy569uyJ66+/HocPH66ipZnnn38e3bt3V8FGGRkZ+PhjmUSUlpZiwoQJSEhIQGRkJIYPH35S4BAhhBDSIAlSeoDGQECTl7vvvlsF5v7444/IysrCJZdcgm3btiErK6va/TRv3hyPPPIIcnJy8O2332LgwIG44oorsGHDBgDA5MmT8f777+PNN9/EqlWrsGfPHlx11VWBDJkQQgghjYQaWaWPs23bNnTpcmzztv/+97+47LLL8PDDD2PdunW45JJLqt3PZZddZvr5oYcewvPPP4/Vq1ejefPmmD9/PhYtWoSBA49t+rZw4UJ07twZq1evxjnnnBPI0AkhhJB6QTACdhsLAa28OJ1OlZjxs88+w0UXHYsRiY+PD9gq7fF4sHjxYhQXFyMjIwM5OTmoqKgwbTncqVMntGzZssoth8vKymjdJoQQUv85vsNubV4hSkArLwMGDEBWVhbOPfdcrF27Fm+88QYA4Ndff0Xz5s1r1NePP/6IjIwMlJaWIjIyEu+88w66dOmC9evXw+l0IjY21lT/VFsOz5o1y3dyyLO7AHY3Oj0ugaH3jhmp9Jbr5ik973rZvj3iPXMMz3+Wy/b7D1/3g9LTzumgdJOVvyudZo9SOleaIn6d3PrDQ+V8Nm3X4vgN5ksoulwmYlanBIAmWCVo9kia1P/6SDulr49frXT4XqlT6JUAYSO/QOmNheYNiyz2g0q7RMITHSZjL5S+yqO0gN1yCeR1RYq22CTotYlTC9h1J5rOHWuV++MJ0wN2pX2l2/cvsR7Iq6cBsGiBuRVauR6UCwAV2h8Ht00CcPXt/vWAXT09gFMLzC0w5D45LNq5tSDbE9MDVOqpA/wE5urfPsyBuYI3gC39a5oGwG8wbSDfDLU21up8v6rpH/Az8Qc/hP9TITUgSAG7jYGAVl6effZZ2O12vPXWW3j++efRrNkxZ8rHH3+MIUOG1Kivjh07Yv369VizZg1uueUWjB49Gj///HMgwwIATJkyBQUFBeq1a9eugPsihBBCSP0joJWXli1b4oMPPjip/KmnnqpxX06nE+3aHVsh6NOnD7755hs8/fTTuPbaa1FeXo78/HzT6ktV2xgDx3I6hEJeB0IIIQ0bxrwETkCTF+BYjMqSJUuwceOxjMZnnXUWLr/8cti0RwGB4PV6UVZWhj59+sDhcGD58uUYPnw4AGDTpk3YuXMnkz8SQghp+PCxUcAENHnZsmULLrnkEuzevRsdO3YEcCzWpEWLFvjwww/Rtm3bavUzZcoUXHzxxWjZsiWOHDmCRYsWYeXKlfjkk08QExODsWPHIisrC/Hx8YiOjsZtt92GjIwMOo0IIYSQECagycvtt9+Otm3bYvXq1SplwMGDB3HDDTfg9ttvx4cfflitfvbt24dRo0Zh7969iImJQffu3fHJJ5/gwgsvBHDsMZTVasXw4cNRVlaGwYMH47nnngtkyIQQQkj9opaPjbjyUkNWrVplmrgAQEJCAh555BGce+651e5n/vz5VR53u92YO3cu5s6dG8gwTeyd5IEtvBLNr92myjq+KK6PWReJW+hffRcofdfgCaZ+Wr9XpvSyYXL7dmWKbvOJBAlvrhAXzTlnb1L64P+JK+ujYtFGG9mWP/4nSQEAAGfftlnpbU3FkVNhiKulsrU4ftbub6X0/UmSpiByr1z3Lo98+o0i2YZ/68FU07lbRki/7sPSpjxGXE/hu8WGVBEtqQ0MLVFnTLikCrA4xbGT5Dgg9cPMe/pHWcWhVBkmMeYui9xzj58wJ69LxuoxxM1jc2qOH0N3G4mj6NgxOV+Y5jaqgDweDbfJZ6LUK9cUbpXyMq1cdyHp7iTHiW4j7dwmt5FebvFTX0+dYEonIFp3DtlOSLSq/03UHT+GyQlUG0fNmXD8nP5TkOoRyrEZVcLHRgETkNvI5XLhyJEjJ5UXFRXB6fSXTIYQQgghpPYENHm59NJLMX78eKxZswaGYcAwDKxevRo333wzLr/88roeIyGEENL4YG6jgAlo8jJnzhy0a9cO/fv3h9vthtvtxrnnnot27drh6aefrusxEkIIIY2O41bp2rxClRrFvHi9Xjz++ON47733UF5ejmHDhmH06NGwWCzo3Lmz2q+FEEIIIeR0UaPJy0MPPYTp06cjMzMTYWFh+OijjxATE4MFCxacujEhhBBCSB1Qo8nLa6+9hueeew5//etfARxLyjh06FC8/PLLsFoDegJ1xvis9/8hOsqKP4yfrMqSnl+j9OuLBil9722/KL3rOrP7pP1fJOHQpPXXKj3gD1K+L0V2AH52/wVSP2WZ0tM2Sfn8nQOUPnqW5DaK+2Cj6dyXxq1X+p9trld6t0ecQN1a7FH6+y0tlE7oLvmPwnKl/s9lKUp7tRxExQekPgBYIrX2B8WpUxanuW6Kpd+KaLNz5jiJ4eK+8rjFItTELnmbvGFm61CUlguoMkzP36M5atw+TwevU8ZRCenHYcptJGuvYQ7z+11qyK9ImFXLbaS5h9x+3ENOUw4j3VWkXY8pf5H/3EZW3VWk50Py4x7S8VfuL+dRIH3pz95tFt2ddOr6Jx+rWZuGtHQe0Fgb0PWRGkK3UcDUaMaxc+dOXHLJJernzMxMWCwW7Nmzp4pWhBBCCDkRxrwETo0mL5WVlXC7zV9xHQ4HKioq/LQghBBCCKlbavTYyDAM3HjjjabEh6Wlpbj55psRESGPFd5+++26GyEhhBDSWAnh1ZPaUKPJy+jRo08qu+GGG+psMIQQQkjIwJiXgKnR5GXhwoWnaxyEEEIIIdUioNxGDZH/lcYh3GHDyJs/VWXv/S4Oo1av/Kb0jBHdlH65/6umfh5zSe6myPejlL5/5sdKjx5wp5zjO3H8PD30G6U9WnqFXeu7Km05S5wW0f/KN537bNdhpfPbhymdUyZ5iAY2EafUL5/7zu5t2y/Onu9KWvms49pn/mgY0ZFy7KC4ko40D1faW1Ki1RcHDjT3SdMwue5ct9y/BLvmQgo3n9ut5d3R3UYOPbeRH7eR4RIHj0dLzONyyvhKNXdL+Am5jco191CYrVxrI2kwXJoLqcQr5Q5/OYyg51WyavWrmdvI6ztUzaOVm5xYXt8OLa+3ivxC/r7R1cKFVG+o0ukUQBtCAqS2QbehHLAbMpMXQgghpF7Bx0YBU783ZyGEEEIIOQGuvBBCCCFBgI+NAoeTF0IIISQY8LFRwPCxESGEEEIaFCGz8vLAG9fD5nZjw/i5quyt8b2U9n4qDpw33j1P6Rk3Sc4iAJg6qIvSTT7ZpnTbh8WNsydTXCOJX8st3jdEHDW2+Dips06mz0evE0eR1WW20CRYZSPAgg7SZnm+jGl84iqlX/19qNKHvZoT6FC+0t8dbi7nc+5X2n3AdGp44sRVZDssfZXFSLmh5UYKiyqVfh1yD1JdBUrnhiUqnWAtVrryBLdRuOYq0t1GpvG55X54tRxBVpeew0jK9RxGFbrbSHMUAUCp4fB5zJzbSPo67JX3yK25kMq8cg16zqNyrdx2wteoSpN7SNCdPXq5x497qDp5iqwnfI8xH9PzJ/nsqopvgKd2IZ18bn/nqMIdVRf1A+FMnIM0XrjyEjAhM3khhBBC6hOMeQkcTl4IIYSQYMCVl4BhzAshhBBCGhRceSGEEEKCAVdeAiZkJi9p8zbBbnXiivMvUWUfdpet/4dcf5fSbd6QaNWnh6eZ+vn9cgm2bL8kV+mVpRK4NzI9W+l1T3VSemFBT6Uru8i2/HHfHZRxTJEA4TUtpC0AHDXKlHa2lwDjr/e0VvqxFAnYjdolAaNbKiTA1KulJti2T8bRNlICccP3mX8ryhJk2/uIHfuULo9torThkXuTECUBuBantE1x7pX64ZKdPMoqwbCV4eYFQYdFttav9JMGQA/YrTBkHA6XbNFfZkjAbrhD3+pfTwFgTg+gB+zqaQD07f7DrWU+y/U0AHrArlV7UF3plfonBuyW68e0uFBT2gAtdYJXC461mYJsLT7rV7lLfg0Dc/2mB/B7ggBSE9RVfXLaCOUYjEBgzEvg8LERIYQQQhoUnLwQQgghwcCog9dp4tChQxg5ciSio6MRGxuLsWPHoqioqMr6t912Gzp27IiwsDC0bNkSt99+OwoKCkz1LBbLSa/FixfXeHwh89iIEEIIqU/U58dGI0eOxN69e7Fs2TJUVFRgzJgxGD9+PBYtWuSz/p49e7Bnzx488cQT6NKlC3bs2IGbb74Ze/bswVtvvWWqu3DhQgwZMkT9HBsbW+PxcfJCCCGEEMXGjRuxdOlSfPPNN+jbty8A4JlnnsEll1yCJ554AqmpqSe16dq1K/773/+qn9u2bYuHHnoIN9xwAyorK2G3y3QjNjYWycnJtRojHxsRQgghwaCOHhsVFhaaXmVlZagN2dnZiI2NVRMXAMjMzITVasWaNWuq3U9BQQGio6NNExcAmDBhApo0aYJ+/fphwYIFMPw6BPwTMisvljA3LFYXjjwh2+Efmis3rMNfNip94GXZov/Zj2VpCwDuv/Qdpd/sNVDpBza3UXrJWf9SeuRmcdrM/6m/0uF9ZFv9lOd/VvrK6HVKL+8ywHTunyvEoTGo5WalP/iqt9KRZ4sdJ2y3uIq+OSqOJN0VVJkbprQlOlra7je7boqaadvkF2lb+cdWwhcp4XLuojAZU5JDnn96InS3kTiBKiKqcBuFwyeGS9rr6QFcThlfufYLEm6X6ys15Ncgwm7+pTelB7CW+yyPt8pzYD1tgJ4GoFJzITmhl2spAE4w4OjpAXT3kMeUNqA65TVLG1DVMbNzqRrffepwWbvOlsjPgEMjoLGGsHMkZKkjq3SLFi1MxdOmTcP06dMD7jY3NxdNmzY1ldntdsTHxyM3N9dPKzMHDhzAgw8+iPHjx5vKZ86ciYEDByI8PByffvopbr31VhQVFeH222+v0RhDZvJCCCGENEZ27dqFaO3Lp8vl8lnvvvvuw6OPPlplXxs3bqzyeHUoLCzE0KFD0aVLl5MmUQ888IDSvXr1QnFxMR5//HFOXgghhJCGgAV+U5dWuz0AREdHmyYv/rjzzjtx4403VlmnTZs2SE5Oxr59+0zllZWVOHTo0CljVY4cOYIhQ4YgKioK77zzDhwOR5X109PT8eCDD6KsrMzvpMsXnLwQQgghweAM77CbmJiIxMTEU9bLyMhAfn4+cnJy0KdPHwDAihUr4PV6kZ6e7rddYWEhBg8eDJfLhffeew9ut59dRTXWr1+PuLi4Gk1cAE5eCCGEkKBQX63SnTt3xpAhQzBu3DjMmzcPFRUVmDhxIkaMGKGcRrt378agQYPw2muvoV+/figsLMRFF12EkpIS/Otf/1LBw8CxSZPNZsP777+PvLw8nHPOOXC73Vi2bBkefvhh3HXXXVUNxyecvBBCCCHExOuvv46JEydi0KBBsFqtGD58OObMmaOOV1RUYNOmTSgpOZZWZt26dcqJ1K5dO1Nf27ZtQ1paGhwOB+bOnYvJkyfDMAy0a9cOTz75JMaNG1fj8YXM5GXzbS1hdbvRNkvyDl0ycoLSP583X+nLe49Sut0iySEEADdeL7l5nrg0Vmnr51Inuqssf1ns8rwv4usIpQt6i6slqUIcMZ2054MHu5rfno8Luyt9adx3Sq/cJnY2Pf+RJU9yJn2VLx8mi02uKWyvOEa8CVFKOw9IniMAKO0aK/WK5Zg9VjufTRw1zcPFsfVLuOwJ0NQmLqTKSM3JY3IUmZ8CWzVHv0dbhdRdRXCLg6dUy23kdoirqExzykQ6yrT6vh1FgNk95LZIXwWecJ/leg4jh0XLq+Txnduo3KPnQjJ/jTK7h3yX6/hzG/p1DvlxIR07WNNcRacu19/HKp2RdZUniY4fUt+px4kZ4+Pj/W5IBwBpaWkmi/P5559/SsvzkCFDTJvT1YaQmbwQQggh9Q5OmAOCm9QRQgghpEHBlRdCCCEkCNTXgN2GACcvhBBCSDCoxzEv9R0+NiKEEEJIgyJkVl4WXjYPkVFW3LXsVlWW9oK4Ulb2ExvLlhGyU2Gbe8SdBABbK8Rp0+ti2Ub54CTJmfTfv2ibAHWRnEfJX4nLJ+PPW5Te0SzF55hLzzpq+vnjPV2UvqVrjtIx2+U6fqsU7c2XPELf57ZXumWkuHQicmXqXpooeY7CfxZXFQCUJcQqbVSKuyY+Wu6HRdtkqKVL2m+MbCv1bVK/IlKcNuEWcfVU+MlfBACecBmvx5DrsLvF2VOhlUc49XxEcr4Iu+88RZH2UtP5SrxyTeFWcSjtrYhV2qm7ikxuIxmHnsPICd/lthNzG5mOabmK9PxC2v6cXq/v+l6tvj/Hj/WEfT79mgb8ftOrzT6h1SSY3zJr6oBq5ITy44q6hI+NAidkJi+EEEJIvYKPjQKGj40IIYQQ0qDgygshhBASBPjYKHA4eSGEEEKCAR8bBUzITF6SbGWIslnhvmePKvNesFvpv378F6UnDv1E6c8Wytb7AHD7b82UfrndG0rftH6g0jN/GKq0O0O23E9aKFv6j038n9J3dZMg4l8qJBh2YPtfTef+bG1XpRO6S6qBiG2y5f7XJRIc6y2XoNSjv8s4LLESkByxV4JNS5Lk4xCWLX0CQHm8B75oEZ0v7SMk0jbVIekBvJESDB1rlX4qIuWppUNPDyCXdhLeMGlfCdEul1xHqRZtGuWUINtiLTA3wqaVm4JyT0gPoLWJtxZJuSltgJy7XAvYdWrj08utWuxnpR5ke0LQq780AP7L/aQB8JceoKogVO2Pos2iB/nWcCv+ALbub0jfJms81gZ0beQMwMlLwDDmhRBCCCENiqBOXmbNmoWzzz4bUVFRaNq0KYYNG4ZNmzaZ6pSWlmLChAlISEhAZGQkhg8fjry8vCCNmBBCCKkbjse81OYVqgR18rJq1SpMmDABq1evxrJly1BRUYGLLroIxcXFqs7kyZPx/vvv480338SqVauwZ88eXHXVVUEcNSGEEFIHGHXwClGCGvOydOlS08+vvPIKmjZtipycHJx33nkoKCjA/PnzsWjRIgwceCymZOHChejcuTNWr16Nc845JxjDJoQQQkgQqVcxLwUFx3aEjY+PBwDk5OSgoqICmZmZqk6nTp3QsmVLZGdn++yjrKwMhYWFphchhBBS37AYRq1foUq9cRt5vV5MmjQJ5557Lrp2Peaqyc3NhdPpRGxsrKluUlIScnNzffYza9YszJgx46TyIV/cAmuYG1sufFmVXZI+WulOL8hW+llXbVP6pRFDTP04PmyidJPJ4qKBTdwy7hXi7CnIkO3mE58Xh0svp9z6fX3EufJWgbibrk1YYzr32s09lD5qSF/WvfuVXnGos9IWW77S4b/LPNWTGKu0K1ce0R08S8q9xbKNPwA4EuQ6LNq1to44qPSGcHFiJdvlflZEO5WOssh1l0f63ra+8oT0AF5tO324xcFTaoiOcIlLqExzuEQ6yrT6cp+jtTQAegqACC0FAADsr5T30m0RJ5g5DYCWHsDjOz1AuUfumUNb663Qyk/8JqG7iqymNAAWn+V+XUV+XEhVbXnvv42/BiL9pSCo7rn9DyrwMVWrvKpzEHI6oNsoYOrNysuECRPw008/YfHixbXqZ8qUKSgoKFCvXbt21dEICSGEEFIfqBcrLxMnTsQHH3yAL774As2bS4LD5ORklJeXIz8/37T6kpeXh+TkZJ99uVwuuLQEgYQQQkh9hDvsBk5QV14Mw8DEiRPxzjvvYMWKFWjdurXpeJ8+feBwOLB8+XJVtmnTJuzcuRMZGRlneriEEEJI3UG3UcAEdeVlwoQJWLRoEd59911ERUWpOJaYmBiEhYUhJiYGY8eORVZWFuLj4xEdHY3bbrsNGRkZdBoRQgghIUpQJy/PP/88AOD88883lS9cuBA33ngjAOCpp56C1WrF8OHDUVZWhsGDB+O55547wyMlhBBC6hY+NgqcoE5ejGrYvNxuN+bOnYu5c+fW6lwdZhfBbqvAM33bqLJf/youmPZ/+UHplaXiOPjTFZKDCADWjeik9Owbxdnj6dNR6ZQV4v4ZdvMGpde0lbZlxmqlLb3FmfPu9m5K390nx3TuuE3idvmpXHMPHTwk4/td8h+1jRG3S+RuudelKWFKR6z7XemjTWOVNirlXACQFCeWc2uYtE9z7ZAxRXdQOtEmLqbyKHHUuDS3UYWfHEaVEV7TzxWaq8gZrjl+DKkX5RL3ULEh54iy+8lhpOU20vMUhZ/gNioz5TCScx/1SLnJVaS5kGzamm65V+6BTc9tZGi5jSwn5DbSnC82P64ivY3X8O3e0n/NTO4k82024T/vUQ2dS4H8cQ3hP8j1jVD+z/GMQLdRwNSLgF1CCCEk1ODKS+DUG6s0IYQQQkh14MoLIYQQEgz42ChgOHkhhBBCgkQoP/qpDXxsRAghhJAGRcisvBhbd8KwOPDKCxersjcmz1H6notvUfqm7F5KbzxfciEBwKW/iGvkhZUDlXYPkvIWM8W5NDZurdIfZfxR6VWlkjNnZPtvlX7l/UFKR56t5U4CEL5VXEUfH+ku1+YRN46xTSw8lsQE6WuXuHEOdxK3UNjhfKUrm0p+oBNpGy05jPZFRSrdynlA2sfIeOOt8nWiLEbmyA6L3KcKuQUmvOEe08+62yjMLWMs1Ww00U7dVSQusmiH7xxGUVbf5Yl2cyLPEo/05S+3kRMyPt1V5NC+UlVqeYp051ClKbfRCW4jP7mNPH7yDvnLR2R4fX9HMbuWTqgTSF6gGhDQt826zGFUQ2o8Xn6bJtXBMKpIAFbN9iFKyExeCCGEkPoE3UaBw8dGhBBCCGlQcOWFEEIICQZ0GwUMJy+EEEJIELB4j71q0z5U4WMjQgghhDQoQmblJW9ML9hcbiS/sE6Vdb5HXCKH/iq5eJq/LG6aH841T23trVoonfa+HAubsktp4+lopZvZxFKzL0Pqv7RHnEf/bPW20u9tEAfTzsojpnN7d+9VeunuLkrHhO1WOmq71C9LjVHateuw0iXni9vIe1RcNzFNipS2usxOpw4ReXId0ZJ/qZk9X+nyWHHmRFkl9095tO+cO5WRsuZZYVQqbQsXDQBlmpsnyq25igxx6phcRYbmNrIdVfqIV64pyib1d5Q1UdptNed0Oqq5jZwW3VUkvzqm3EYm95BQ4fVdrjuHbCe6jTy+v1t4TS4kPYeRfp/1XEg+uznFkvOpcxX5y59krh9AziO/eZXqiNPdfwMklAM/gwofGwUMV14IIYSQIHDcbVSb1+ni0KFDGDlyJKKjoxEbG4uxY8eiqKioyjbnn38+LBaL6XXzzTeb6uzcuRNDhw5FeHg4mjZtirvvvhuVlZV+evRPyKy8EEIIIfWKerzPy8iRI7F3714sW7YMFRUVGDNmDMaPH49FixZV2W7cuHGYOXOm+jk8PFxpj8eDoUOHIjk5GV9//TX27t2LUaNGweFw4OGHH67R+Dh5IYQQQohi48aNWLp0Kb755hv07dsXAPDMM8/gkksuwRNPPIHU1FS/bcPDw5GcnOzz2Keffoqff/4Zn332GZKSktCzZ088+OCDuPfeezF9+nQ4nU6f7XzBx0aEEEJIEKirx0aFhYWmV1lZWdUnPgXZ2dmIjY1VExcAyMzMhNVqxZo1a6ps+/rrr6NJkybo2rUrpkyZgpKSElO/3bp1Q1JSkiobPHgwCgsLsWHDhhqNMWRWXkaM/QzuSDs++0zejKs3X6X0R31eVPqmayRodlTOGFM/rqESjJu08Dulp8/7n9L3ZEiqgR/Llyqd2fcnpT9b21XptLYS1Bv7U4HSnxR3MJ3bq30I8jZLkGlcU/mgxmyTgNOiFlqw6TrZ3r80OVE6NSTYtF281CmJlqBlAGjn+kXpVbHpSifatC3zYyUo1WWRgF1/aQA8kRIAW6kF5YaFm9MUFHtljHEuPQBXri/WIfem0CsByTF2KS/W0gCkOrQAZq2fCIv53Ee9ch1uizyXLfVIuZ4GQE8P4LRIYGiFFshr08u1+idiCubV2nj9BJx6/aQH0IP69DQARlWBq3UUgNvQAkGZBoCcUeooYLdFixam4mnTpmH69OkBd5ubm4umTZuayux2O+Lj45Gbm+u33fXXX49WrVohNTUVP/zwA+69915s2rQJb7/9tupXn7gAUD9X1a8vQmbyQgghhDRGdu3aheho+WLtcrl81rvvvvvw6KOPVtnXxo0bAx7H+PHjle7WrRtSUlIwaNAgbN26FW3btg24X19w8kIIIYQEgbrKbRQdHW2avPjjzjvvxI033lhlnTZt2iA5ORn79u0zlVdWVuLQoUN+41l8kZ5+bJV+y5YtaNu2LZKTk7F27VpTnby8Y9tw1KRfgJMXQgghJDicYbdRYmIiEhMTT1kvIyMD+fn5yMnJQZ8+fQAAK1asgNfrVROS6rB+/XoAQEpKiur3oYcewr59+9RjqWXLliE6OhpdunTx141PGLBLCCGEEEXnzp0xZMgQjBs3DmvXrsVXX32FiRMnYsSIEcpptHv3bnTq1EmtpGzduhUPPvggcnJysH37drz33nsYNWoUzjvvPHTv3h0AcNFFF6FLly7485//jO+//x6ffPIJ7r//fkyYMMHvoy5/cPJCCCGEBIH6vEnd66+/jk6dOmHQoEG45JJLMGDAALz4ohhbKioqsGnTJuUmcjqd+Oyzz3DRRRehU6dOuPPOOzF8+HC8//77qo3NZsMHH3wAm82GjIwM3HDDDRg1apRpX5jqEjKPjW6N3YboKCuevVecREkvJyjtmSV1rbGyrX78IrPrpvwv++WHV8R5cbZL3Ce7MuW2zs7LVDoreZnS69d1V3rfFbJroWW7bPW/eLc4owDA6ZJnkNGbZd5Z0VKcR2E78qXfPrI8GKvtjBierKUB0Hz1XaIk/cC3MWanU5rjgNJl8TJDjtFcRaWxvtMAlEfLb5gX4hyyRIh7p0RLDxAdJlv3HzsmfcU6fW/3H6e5jY54xG0UZZW+9lbEKh3hEofWUY/uKDoxPYDuKpKxl1Zq6QE0u0CZR8r1bwaV2lb/Du2I7hCynrAlv980AH7aGH7cRv7Kq9wmv67SAPjtPxCnU03LmQaA1HPqcXqA+Pj4KjekS0tLg6H9QWjRogVWrVp1yn5btWqFjz76qNbj48oLIYQQQhoUIbPyQgghhNQn6sptFIpw8kIIIYQEA69x7FWb9iEKJy+EEEJIMKjHMS/1Hca8EEIIIaRBETIrL9duuQj2CBf+N2i2KrtpvDiPLhn+V6Vdw8Vt1PQVyV8EADOf0HIY/VHPYbRC6YF/+EHpz76RHEbzr/xK6YTvJIfRB8WybbKnQMq3bexsOnfnZJlmx/0qrpjCNHHdxP+4TemS5vFKGx7JHdS5qbiWSqIk8VDXsF+VXpPQ23TuVLu4dkoT5GMTbhW3UnksfOKJEpdOmSHjDo8Ux4+ev6hJWLGpfb6WkyjeKcf0HEZxdinXXUh6DqNfS2UHRz2HUbFH+tfzFwGnP4eR7kLS6wNnIIdRVd/a6iqHUT38ZhhQnEA9vI6aEsrxEfUVC2oZ81JnI2l4hMzkhRBCCKlXnOEddhsTfGxECCGEkAYFV14IIYSQIECrdOBw8kIIIYQEA7qNAoaPjQghhBDSoAiZlZfiOc1gd7ix/1lxj1hTkpRu+kK40q77diht+bc506Wew2jHZTL3m77rcqX/2eptpX/8WnIY7bzsiHS0abuSL28boHRMmOQ2ittgnluWtWmqdNjWg0rnpouLJqagUHRzcS5ZXeLA6R2zU+kvm3RTur0zT+nSRKkPAPGaq6g03k8Oo1j5GlCh5SqyRouzR89hFBeh5SMyxI0T75JywOweSnCIq6jAI+9ZrE3a7CiTXE/tXblKF1fqriJxPfnLXwTUXQ4jj0fPUyT3z1/+IuAM5DDyk78ICCCHUV3Vr7KvGp4jRAnlRwkNDYthwFKLoNvatG3ohMzkhRBCCKlXeP//qzbtQxQ+NiKEEEJIg4IrL4QQQkgQ4GOjwOHkhRBCCAkGdBsFTMhMXlxLc2C3OPCnzDtUmf0meWrW6oGvlX71ZdnG/7Jhd5n6eb/kG6Un/nGZ0i++PUTptLGy5X78agkYfelQhtLeEgkwPfidBOLGtZLgw4SfzIGrh86SANXENdJvaet4+KJ30u9K58bHKt097HulP2/aX+lUmwTTliSat7MPs0iwa2mCz9OhMlbal2mBuVFRR5U+oqUBSAovUjrfI0G5SS4JOj52LELpeLveRu5HG6ekPCjStvuPspRq5RJ0HG6VgN2SSil3nxiwqwXm6mkAyvVAXm0r/nItPYAeZOvRgmn1NAB+t/oH4PX4fqprmPqy+iw3N/BzgqoCXWuaBqCG/QQUsFtDQjUNAGlAcIfdgGHMCyGEEEIaFCGz8kIIIYTUJ7jDbuBw8kIIIYQEAz42Chg+NiKEEEJIg4IrL4QQQkgQsHiPvWrTPlQJmclL+eA+8Drc6PjELlU2bNl3Sr/91vlKOyzZSttHiIsFAO789k9Kb/jDAqU/Xi7tl18vjpPK3yTVwKIfzla6U1MZR+I6+QQWdhHnUPRX20znzr+yrdIJmlupbUvZ1t8WLU6nc2M2Kf1m8kA5t/OA0iXJ4syJs4rj52iifydKeZyMt8wQ1447VnP2aG6jppHiEDrolfMlucVVdMgbqXQTh9QHgIMeORZvl/QAG4+mKh3llnMXVsp1RFglNUFRhZTrrqKSSj09gOnUKDUd09xGmqvIprmKKvVyi+428p0GwOsnBQAAGH7+MPl1FcFPudd3Ogd//R87WIfOpbqCaQBMhHK8Q6OBj40Cho+NCCGEENKgCOrk5YsvvsBll12G1NRUWCwWLFmyxHTcMAxMnToVKSkpCAsLQ2ZmJjZv3hycwRJCCCF1iVEHrxAlqJOX4uJi9OjRA3PnzvV5/LHHHsOcOXMwb948rFmzBhERERg8eDBKS0t91ieEEEIaCsfTA9TmFaoENebl4osvxsUXX+zzmGEYmD17Nu6//35cccUVAIDXXnsNSUlJWLJkCUaMGHEmh0oIIYSQekK9jXnZtm0bcnNzkZmZqcpiYmKQnp6O7Oxsv+3KyspQWFhoehFCCCH1juMBu7V5hSj11m2Um3ssd09SUpKpPCkpSR3zxaxZszBjxoyTyt2374E9wgXjSnGrjIvZK+3+Ki6dm7ZdofTrXV419XPdlLuV3ttfcvbY1/6i9AO/DlM6xr1b6divxe1SdlZLqbNeHE3brk9WOvwdcQUBQFR7cSJZXdLX+U1/VfrL5G5K93Z/qvRrzcSxk2oTB01xisxfHRb5OJQ2Nf9SVGjuIUtCmdJFhrh5EqN1V5H0mxquuYq0fETJTinfXyn3P8lRYDr3jrImSqc59yudXyF9RVvlUeKRSt1V5FHa5CrSHhYfNZWbHS3llTbtmFxTRaWfHEYeP64iU7nFZ/mJ+Mth5Nd1o7mHTK6iABxCfi2YNXYh1bC8qnP4oc7yLdVT6CpqxBgw/d4G1D5EqbcrL4EyZcoUFBQUqNeuXbtO3YgQQgg5wzDmJXDq7eQlOfnYCkReXp6pPC8vTx3zhcvlQnR0tOlFCCGEkMZDvZ28tG7dGsnJyVi+fLkqKywsxJo1a5CRkRHEkRFCCCF1gIFaxrwE+wKCR1BjXoqKirBlyxb187Zt27B+/XrEx8ejZcuWmDRpEv7xj3+gffv2aN26NR544AGkpqZi2LBhwRs0IYQQUhdwh92ACerk5dtvv8UFF1ygfs7KygIAjB49Gq+88gruueceFBcXY/z48cjPz8eAAQOwdOlSuN1uf10SQgghpJET1MnL+eefD6OKmaPFYsHMmTMxc+bMWp/rzfafIDrKiq63T1BlD+w7qPSCIS8pPXn2zUo3uUecKAAQ/6ns8Hv3LcOUNsoOK31kRVOlo7uFKZ30tdT5/cI4pVP/J0HFnrPEdWOxiaMFAC5sIa6iDSktlD4vQqzjn7X8g9Jt7HJvjzSXtzrMIvmFSrTwIa8W9u5pIi4iwOwqahInrqJDHjlHi8h8pfd7IpRuGXZI6dzKWKVTnXI/dLdRV/fvpnOvrxRnVqxVcjrlV8i9jdJyGBWWy+Q2XLPNFFc4lXZreYdKK/T8ReYnqWWVct+sphxGUs+Uw8iPe8ifq8jw+HEUoYocRv5cRX7r+y6v0sVyul1FAeQpauyuIhKCeOE3JVm124co9TbmhRBCCGnM1Ge30aFDhzBy5EhER0cjNjYWY8eORVFRkd/627dvh8Vi8fl688035Zp9HF+8eHGNx1dv93khhBBCSHAYOXIk9u7di2XLlqGiogJjxozB+PHjsWjRIp/1W7Rogb1795rKXnzxRTz++OMn7aS/cOFCDBkyRP0cGxtb4/Fx8kIIIYQEg3oasLtx40YsXboU33zzDfr27QsAeOaZZ3DJJZfgiSeeQGpq6kltbDbbSduYvPPOO7jmmmsQGRlpKo+Nja1yy5PqwMdGhBBCSDCop+kBsrOzERsbqyYuAJCZmQmr1Yo1a9ZUq4+cnBysX78eY8eOPenYhAkT0KRJE/Tr1w8LFiyoMvbVH1x5IYQQQhowJ+bwc7lccLlcfmqfmtzcXDRt2tRUZrfbER8fX2V6Hp358+ejc+fO6N+/v6l85syZGDhwIMLDw/Hpp5/i1ltvRVFREW6//fYajTFkJi8v5KfBXWnHw39+TZVNfWGU0vdP/k7p1Le2KX3HyEGmfioPiHPmp4/PUbpl93ylm6+QD9LeP8gOvynPfav00fvOkk4NCRkf0naj0ltSzXmdLon5SOl1bXsp3cUpeX0KWoujJtoqbpzi5tKP7iqqSBWXTpFXchYlJB4xnfuAR9q0jpF7sNsjLqE24ZKLaXeFuKmaO6V+XkWM0h3d8nx0Q0kzOXeEOSjsULk4l0yuogrfrqIiP66iEq1cdxWV+clfBJhzGPlzFVUvh5FvV5FfhxBgcgmdFldRVeeuy1xFNSRUXUXMYRSC1NFjoxYtWpiKp02bhunTp59U/b777sOjjz5aZZcbN26s8nh1OHr0KBYtWoQHHnjgpGN6Wa9evVBcXIzHH3+ckxdCCCGkQVBHVuldu3aZUuH4W3W58847ceONN1bZZZs2bZCcnIx9+/aZyisrK3Ho0KFqxaq89dZbKCkpwahRo05ZNz09HQ8++CDKyspqtFrEyQshhBASBGprdz7etrp5/BITE5GYmHjKehkZGcjPz0dOTg769OkDAFixYgW8Xi/S09NP2X7+/Pm4/PLLq3Wu9evXIy4ursaPuTh5IYQQQoiic+fOGDJkCMaNG4d58+ahoqICEydOxIgRI5TTaPfu3Rg0aBBee+019OvXT7XdsmULvvjiC3z00Ucn9fv+++8jLy8P55xzDtxuN5YtW4aHH34Yd911V43HyMkLIYQQEgzqqVUaAF5//XVMnDgRgwYNgtVqxfDhwzFnzhx1vKKiAps2bUJJSYmp3YIFC9C8eXNcdNFFJ/XpcDgwd+5cTJ48GYZhoF27dnjyyScxbty4Go8vZCYvry+4EDaXG/+75ylV9tKrst3+qGGyYY4nT571rX3LvETW7GwJZG31vp/t/p/+Rumj93RR2nhWPmhXdv5e6Q1asNU18UuUntrR/Ib2dsqH5HB7WWKLs4YrfSRN6uuBueXNJRi3wCsBvk2bFiid5/Eo3SFuv+ncemBuh8g8pXdVJCjd2i1tfq+IV1rf7v+XoylKD4jYpPSBMtkHINYqYwWAQ2VyfVEWGWNBmQTs6oG5xeVyb/TA3NIK+bjrgbnlWrkelAsAlVrArh406zGVa4G5lX4Ccz2nDrK1orrpAeooMLfK9AA1Lfd3jgBSE/ijEQS0MiiXmPAatftQeE/fByo+Pt7vhnQAkJaW5tPi/PDDD+Phhx/22WbIkCGmzelqA/d5IYQQQkiDImRWXgghhJB6RT1+bFTf4eSFEEIICQq13SU3dCcvfGxECCGEkAYFV14IIYSQYMDHRgETMpOXpIXrYbc4MWDQjaostVRcMNsWdFU6clCF0i3/s8vUz9ax4gxqNfVrpcNmtVfa8pzc1r92/Z/Sn7WXJFd/jpM0BRN73KF0b5ec+8BZsp09YN7uv6CDfGgrjEqlva3FkXTAIzqtmWzd/7vmiOneZI/SWys1h1CUlAPAb+WS56KdW9xGO8qaKJ0esUXpdUWtlM6M/Fnp3FLZSCnBKq6nA2V6CgBxSQHA4TK57girLBYe0VxFbou4f46WO5TWXUVluttIq19RIdoO0QDgqfSz3b8/V5HXz2JmTbf6P6GNjsXrs7jmrqKA3EY12w60sbuK/F0fXUWkWngN1OoDfxrdRvUdPjYihBBCSIMiZFZeCCGEkHqF4TUl5g2ofYjCyQshhBASDBjzEjCcvBBCCCHBgDEvAcOYF0IIIYQ0KEJm5cXSpgUsNheSHhWHSt4N3ZRu+sp3Sm96XvIRtR+z29TPeUMOKb17nuTpmdHpPaX/efb1So+OWa704ozBSndzioNmX29xuIRZZHxHuonzCACKtJxE0e0lr9JOj5T3bCHj3VwhOYH6Ndmh9IbyVKV7R0n5z6XNlO7sNl/310UdlL4iJkfplfkdlb4yZp3Se4/GKJ2g5SrKLZEcSVHa1PnwURlruMXs+DlSKvfEpbmBSkrFjaXnMCor9+0qqtTKdeeQR3Mb6c4hAPBWWH0eMzy+5/1GpR9XkZ/cRhZ/OY8QgKvIb30/5VW6jWqWk8ivuyYQp1M9hO4hclrgY6OACZnJCyGEEFKvMFDLyUudjaTBwcdGhBBCCGlQcOWFEEIICQZ8bBQwnLwQQgghwcDrhf+gtOq2D0342IgQQgghDYqQWXn59Y5IWMPcaD9GXEV9ZovjZ/e7sUo/94d/Kf3PP4hzCAD+kfKs0pcOuUvpIWHlSk8eJP0mWCVnz4FzJQeR7hwK6y0Opq2VRTK+jttN5/6+XNw1FzX/RelvSiXf0qCEjUqvOdpW6XMiJe+QP+fQgv1/UHpw8gbTuf+vqL/SzRKKld5+RPIhJaTKt4C9RZLDKMYqbp5DJeIqirTIx6+gxK20+wS30VGTq0iOlZdJDiM9J1FFmW+3kafcj3Oo0v8c3jDlNtLb+HYV+XMP+XUVVeE28nuspu4hf86hKvIqNWb3UFXOIbqKyBmFj40CJmQmL4QQQki9gpOXgOFjI0IIIYQ0KLjyQgghhAQDpgcIGE5eCCGEkCBgGF4YtcgMXZu2DZ2Qmbws/ePziIqy4k8j71Zl85o/p3S3v0xQWg++vfV6CQoFgCir/Hz0skKld1YeUbrD+b8pvbpMgnSv6CnBwp8cTVR6TNtspT8qktQEI5LWmM79YWFPpYfGfK/0/x2QYNrbmko6gpm/Xybnbvmj0s/vOl/prCb/U/qXw0lKJzczz+i35Utgbnxr+djkFcp2/zHavTlUpG/3L+VFxRKY69ICdsuOOrRy8z0v146ZAnDLfG/rb5RLuSnItsJ38C3K/ZQDsFT6CXb1F+Rb08Dcqv72+Amo9Rdo67/cT/9Vpgeo4liQqGlqAgbfknqPYdRu9YQxL4QQQgghDYOQWXkhhBBC6hVGLWNeQnjlhZMXQgghJBh4vVU8160GIRzzwsdGhBBCCGlQcOWFEEIICQZ8bBQwITN5yfO4UOyxos+kdarsw5JIpceM+FTpeQXNlH544Fumfh450Fvp2d3fkPK8TKVntHpX6Vm/D5W+Wkr5xK3XKP1yO+lnxM+jlF5ylqQpAIBZmy5W+r6e4kS6dXea0k83k8W0n3KTlW7e2qX0tn0JSid0FPdP3gHZ0j/aIvUBIP+QpDmItMqxkkJJhaC7hMoLXT7LK4t055B8/DwlUn6i4wdHfbuHUOqnvMz3gqKl3M9CY0UVW/RX+Gnj14VUs/QAftMGIACX0Bn4O1ZXjh9u0U8IYHi9MGrx2CiUrdJ8bEQIIYSQBkXIrLwQQggh9Qo+NgoYTl4IIYSQYOA1avecNIQnL3xsRAghhJAGBVdeCCGEkGBgGKg6R0h12ocmITN5GfPhX2F1u7HlmhdUWbv//FXp6pQDQLuPxCU045oNSv/1q55KP3eNOIFyvm2ndNs24m7a/H0LpZt1lPxAezc2VTqhmzh8ACB/S5zS0b3F5XN0h7QPSxeXT8Xv0l53/Hhzpa3u+MEBl+9yANbDvt1A1ny77/Ij4gTSsRX7KS/xvwhoLfV9zFrm26ljLfdT7sdVZPXjEAIAq6dm5f6MA37LA3Dd0PFDSOPA8BowavHLZnDyQgghhJAziuFF7VZeaJWu18ydOxdpaWlwu91IT0/H2rVrgz0kQgghpNHy0EMPoX///ggPD0dsbGy12hiGgalTpyIlJQVhYWHIzMzE5s2bTXUOHTqEkSNHIjo6GrGxsRg7diyKiopqPL56P3l54403kJWVhWnTpmHdunXo0aMHBg8ejH379gV7aIQQQkjAGF6j1q/TRXl5Oa6++mrccsst1W7z2GOPYc6cOZg3bx7WrFmDiIgIDB48GKWlparOyJEjsWHDBixbtgwffPABvvjiC4wfP77G46v3k5cnn3wS48aNw5gxY9ClSxfMmzcP4eHhWLBgQbCHRgghhASO4a396zQxY8YMTJ48Gd26davepRgGZs+ejfvvvx9XXHEFunfvjtdeew179uzBkiVLAAAbN27E0qVL8fLLLyM9PR0DBgzAM888g8WLF2PPnj01Gl+9jnkpLy9HTk4OpkyZosqsVisyMzORnZ3ts01ZWRnKysrUzwUFBQAA7/+f+RUekUhLrzYbrE55IG3qqpzn5rl5bp6b5z795y4sOjYhOBPBsJWoqNUedZWoAAAUFhaayl0uF1wul68mp41t27YhNzcXmZmSKicmJgbp6enIzs7GiBEjkJ2djdjYWPTt21fVyczMhNVqxZo1a3DllVdW/4RGPWb37t0GAOPrr782ld99991Gv379fLaZNm3a8S0L+eKLL7744iug165du07b/21Hjx41kpOT62SckZGRJ5VNmzatzsa6cOFCIyYm5pT1vvrqKwOAsWfPHlP51VdfbVxzzTWGYRjGQw89ZHTo0OGktomJicZzzz1Xo3HV65WXQJgyZQqysrLUz/n5+WjVqhV27tyJmJiYII7szFJYWIgWLVpg165diI6OPnWDRgKvm9cdCvC6T991G4aBI0eOIDU19bT0DwButxvbtm1DeXl5rfsyDAMWi3m7B3+rLvfddx8effTRKvvbuHEjOnXqVOtxnW7q9eSlSZMmsNlsyMvLM5Xn5eUhOTnZZxt/y2UxMTEh9Ut+nOjoaF53CMHrDi143aeHM/FF1+12w+12n/bz6Nx555248cYbq6zTpk2bgPo+/n9yXl4eUlJSVHleXh569uyp6pxotqmsrMShQ4f8/p/uj3o9eXE6nejTpw+WL1+OYcOGAQC8Xi+WL1+OiRMnBndwhBBCSAMiMTERiYmJp6Xv1q1bIzk5GcuXL1eTlcLCQqxZs0Y5ljIyMpCfn4+cnBz06dMHALBixQp4vV6kp6fX6Hz13m2UlZWFl156Ca+++io2btyIW265BcXFxRgzZkywh0YIIYQ0Snbu3In169dj586d8Hg8WL9+PdavX2/ak6VTp0545513AAAWiwWTJk3CP/7xD7z33nv48ccfMWrUKKSmpqrFh86dO2PIkCEYN24c1q5di6+++goTJ07EiBEjavyYrl6vvADAtddei/3792Pq1KnIzc1Fz549sXTpUiQlJVWrvcvlwrRp08545HWw4XXzukMBXjevm5wepk6dildffVX93KtXLwDA559/jvPPPx8AsGnTJuXoBYB77rkHxcXFGD9+PPLz8zFgwAAsXbrU9Hjs9ddfx8SJEzFo0CBYrVYMHz4cc+bMqfH4LIYRwskRCCGEENLgqPePjQghhBBCdDh5IYQQQkiDgpMXQgghhDQoOHkhhBBCSIOiUU9e5s6di7S0NLjdbqSnp2Pt2rXBHlKdMmvWLJx99tmIiopC06ZNMWzYMGzatMlUp7S0FBMmTEBCQgIiIyMxfPjwkzb9a+g88sgjyqZ3nMZ63bt378YNN9yAhIQEhIWFoVu3bvj222/VcaMaKekbGh6PBw888ABat26NsLAwtG3bFg8++KAp90xjuO4vvvgCl112GVJTU2GxWFQyu+NU5xoPHTqEkSNHIjo6GrGxsRg7dqzJ2lofqeq6KyoqcO+996Jbt26IiIhAamoqRo0adVISv4Z43aR2NNrJyxtvvIGsrCxMmzYN69atQ48ePTB48OCTdvdryKxatQoTJkzA6tWrsWzZMlRUVOCiiy5CcXGxqjN58mS8//77ePPNN7Fq1Srs2bMHV111VRBHXbd88803eOGFF9C9e3dTeWO87sOHD+Pcc8+Fw+HAxx9/jJ9//hn//Oc/ERcXp+pUJyV9Q+PRRx/F888/j2effRYbN27Eo48+isceewzPPPOMqtMYrru4uBg9evTA3LlzfR6vzjWOHDkSGzZswLJly/DBBx/giy++wPjx48/UJQREVdddUlKCdevW4YEHHsC6devw9ttvY9OmTbj88stN9RridZNaUqNMSA2Ifv36GRMmTFA/ezweIzU11Zg1a1YQR3V62bdvnwHAWLVqlWEYhpGfn284HA7jzTffVHU2btxoADCys7ODNcw648iRI0b79u2NZcuWGX/84x+NO+64wzCMxnvd9957rzFgwAC/x71er5GcnGw8/vjjqiw/P99wuVzGv//97zMxxNPC0KFDjb/85S+msquuusoYOXKkYRiN87oBGO+88476uTrX+PPPPxsAjG+++UbV+fjjjw2LxWLs3r37jI29Npx43b5Yu3atAcDYsWOHYRiN47pJzWmUKy/l5eXIyckxpea2Wq3IzMxEdnZ2EEd2ejm+WVB8fDwAICcnBxUVFab70KlTJ7Rs2bJR3IcJEyZg6NChpusDGu91v/fee+jbty+uvvpqNG3aFL169cJLL72kjp8qJX1DpX///li+fDl+/fVXAMD333+PL7/8EhdffDGAxnvdOtW5xuzsbMTGxqJv376qTmZmJqxWK9asWXPGx3y6KCgogMViQWxsLIDQuW5ipt7vsBsIBw4cgMfjOWkX3qSkJPzyyy9BGtXpxev1YtKkSTj33HPRtWtXAEBubi6cTqf6JT9OUlIScnNzgzDKumPx4sVYt24dvvnmm5OONdbr/u233/D8888jKysLf/vb3/DNN9/g9ttvh9PpxOjRo9W1+frcN+Trvu+++1BYWIhOnTrBZrPB4/HgoYcewsiRIwGg0V63TnWuMTc3F02bNjUdt9vtiI+PbzT3obS0FPfeey+uu+46lZgxFK6bnEyjnLyEIhMmTMBPP/2EL7/8MthDOe3s2rULd9xxB5YtW3bGs7IGE6/Xi759++Lhhx8GcGy77p9++gnz5s3D6NGjgzy608d//vMfvP7661i0aBHOOussrF+/HpMmTUJqamqjvm5ipqKiAtdccw0Mw8Dzzz8f7OGQINMoHxs1adIENpvtJHdJXl5ejdNuNwQmTpyIDz74AJ9//jmaN2+uypOTk1FeXo78/HxT/YZ+H3JycrBv3z707t0bdrsddrsdq1atwpw5c2C325GUlNQorzslJQVdunQxlXXu3Bk7d+4EYE5Jr9PQr/vuu+/GfffdhxEjRqBbt27485//jMmTJ2PWrFkAGu9161TnGpOTk08yJFRWVuLQoUMN/j4cn7js2LEDy5YtU6suQOO+buKfRjl5cTqd6NOnD5YvX67KvF4vli9fjoyMjCCOrG4xDAMTJ07EO++8gxUrVqB169am43369IHD4TDdh02bNmHnzp0N+j4MGjQIP/74o8pyun79evTt2xcjR45UujFe97nnnnuSFf7XX39Fq1atAJhT0h/neEr6hnzdJSUlsFrNf6psNhu8Xi+AxnvdOtW5xoyMDOTn5yMnJ0fVWbFiBbxeL9LT08/4mOuK4xOXzZs347PPPkNCQoLpeGO9bnIKgh0xfLpYvHix4XK5jFdeecX4+eefjfHjxxuxsbFGbm5usIdWZ9xyyy1GTEyMsXLlSmPv3r3qVVJSourcfPPNRsuWLY0VK1YY3377rZGRkWFkZGQEcdSnB91tZBiN87rXrl1r2O1246GHHjI2b95svP7660Z4eLjxr3/9S9V55JFHjNjYWOPdd981fvjhB+OKK64wWrdubRw9ejSII68do0ePNpo1a2Z88MEHxrZt24y3337baNKkiXHPPfeoOo3huo8cOWJ89913xnfffWcAMJ588knju+++U66a6lzjkCFDjF69ehlr1qwxvvzyS6N9+/bGddddF6xLqhZVXXd5eblx+eWXG82bNzfWr19v+jtXVlam+miI101qR6OdvBiGYTzzzDNGy5YtDafTafTr189YvXp1sIdUpwDw+Vq4cKGqc/ToUePWW2814uLijPDwcOPKK6809u7dG7xBnyZOnLw01ut+//33ja5duxoul8vo1KmT8eKLL5qOe71e44EHHjCSkpIMl8tlDBo0yNi0aVOQRls3FBYWGnfccYfRsmVLw+12G23atDH+/ve/m/7zagzX/fnnn/v8fR49erRhGNW7xoMHDxrXXXedERkZaURHRxtjxowxjhw5EoSrqT5VXfe2bdv8/p37/PPPVR8N8bpJ7bAYhrZNJSGEEEJIPadRxrwQQgghpPHCyQshhBBCGhScvBBCCCGkQcHJCyGEEEIaFJy8EEIIIaRBwckLIYQQQhoUnLwQQgghpEHByQshpFps374dFosF69evD/ZQCCEhDicvhDQQbrzxRlgsFlgsFjgcDiQlJeHCCy/EggULVJ6fujzXsGHD6rRPQgipKzh5IaQBMWTIEOzduxfbt2/Hxx9/jAsuuAB33HEHLr30UlRWVgZ7eIQQckbg5IWQBoTL5UJycjKaNWuG3r17429/+xveffddfPzxx3jllVcAAPn5+bjpppuQmJiI6OhoDBw4EN9//73qY/r06ejZsydeeOEFtGjRAuHh4bjmmmtQUFCgjr/66qt499131UrPypUrVfvffvsNF1xwAcLDw9GjRw9kZ2efyVtACCGcvBDS0Bk4cCB69OiBt99+GwBw9dVXY9++ffj444+Rk5OD3r17Y9CgQTh06JBqs2XLFvznP//B+++/j6VLl+K7777DrbfeCgC46667cM0116hVnr1796J///6q7d///nfcddddWL9+PTp06IDrrruOqz6EkDMKJy+ENAI6deqE7du348svv8TatWvx5ptvom/fvmjfvj2eeOIJxMbG4q233lL1S0tL8dprr6Fnz54477zz8Mwzz2Dx4sXIzc1FZGQkwsLC1CpPcnIynE6nanvXXXdh6NCh6NChA2bMmIEdO3Zgy5YtwbhsQkiIwskLIY0AwzBgsVjw/fffo6ioCAkJCYiMjFSvbdu2YevWrap+y5Yt0axZM/VzRkYGvF4vNm3adMpzde/eXemUlBQAwL59++rwagghpGrswR4AIaT2bNy4Ea1bt0ZRURFSUlJMMSrHiY2NrZNzORwOpS0WCwDUuduJEEKqgpMXQho4K1aswI8//ojJkyejefPmyM3Nhd1uR1pamt82O3fuxJ49e5CamgoAWL16NaxWKzp27AgAcDqd8Hg8Z2L4hBBSYzh5IaQBUVZWhtzcXHg8HuTl5WHp0qWYNWsWLr30UowaNQpWqxUZGRkYNmwYHnvsMXTo0AF79uzBhx9+iCuvvBJ9+/YFALjdbowePRpPPPEECgsLcfvtt+Oaa65BcnIyACAtLQ2ffPIJNm3ahISEBMTExATzsgkhxAQnL4Q0IJYuXYqUlBTY7XbExcWhR48emDNnDkaPHg2r9VgI20cffYS///3vGDNmDPbv34/k5GScd955SEpKUv20a9cOV111FS655BIcOnQIl156KZ577jl1fNy4cVi5ciX69u2LoqIifP7551Wu5BBCyJnEYhiGEexBEELOHNOnT8eSJUu4zT8hpMFCtxEhhBBCGhScvBBCCCGkQcHHRoQQQghpUHDlhRBCCCENCk5eCCGEENKg4OSFEEIIIQ0KTl4IIYQQ0qDg5IUQQgghDQpOXgghhBDSoODkhRBCCCENCk5eCCGEENKg4OSFEEIIIQ2K/wcnumZWHU5nbgAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "class TransformerEmbedding(nn.Module):\n",
    "    def __init__(self, config):\n",
    "        super().__init__()\n",
    "        # hyper params\n",
    "        self.vocab_size = config[\"vocab_size\"]\n",
    "        self.hidden_size = config[\"d_model\"]  # 词向量维度\n",
    "        self.pad_idx = config[\"pad_idx\"]\n",
    "        dropout_rate = config[\"dropout\"]\n",
    "        self.max_length = config[\"max_length\"]\n",
    "\n",
    "        # layers,设置padding_idx可以让pad的词向量全为0\n",
    "        self.word_embedding = nn.Embedding(\n",
    "            self.vocab_size, self.hidden_size, padding_idx=self.pad_idx\n",
    "        )\n",
    "        self.position_embedding = nn.Embedding(\n",
    "            self.max_length, self.hidden_size,\n",
    "            # 位置编码，权重通过get_positional_encoding函数计算得到\n",
    "            _weight=self.get_position_encoding(self.max_length, self.hidden_size)\n",
    "        )\n",
    "\n",
    "        self.position_embedding.weight.requires_grad_(False)  # 不更新位置编码的权重\n",
    "        self.dropout = nn.Dropout(dropout_rate)  # 随机失活层\n",
    "\n",
    "    def get_word_embedding_weight(self):\n",
    "        return self.word_embedding.weight\n",
    "\n",
    "    # 计算位置信息\n",
    "    # 用于定义类方法（class method）。\n",
    "    # 类方法是绑定到类而不是实例的方法，可以通过类本身或类的实例调用。\n",
    "    @classmethod\n",
    "    def get_position_encoding(self, max_length, hidden_size):\n",
    "        # max_length是最大长度，hidden_size是embedding维度相等\n",
    "        pe = torch.zeros(max_length, hidden_size)  # 初始化位置编码\n",
    "        # .unsqueeze(1) 是将这个一维张量转换为二维张量，\n",
    "        # 即将其形状从 (max_length,) 变为 (max_length, 1)。\n",
    "        # 这个操作在张量的维度上增加了一个维度，使其从一维变为二维，第二维的大小为 1。\n",
    "        position = torch.arange(0, max_length).unsqueeze(1)  # 位置信息,从0到max_length-1\n",
    "        # 计算位置编码的权重,为了性能考量（是数学上的对数函数分解），这里用了log函数\n",
    "        # torch.arange(0, hidden_size, 2) 在 PyTorch 中用于创建一个一维张量，包含从 0 开始到（但不包括）hidden_size 的数值，步长为 2\n",
    "        div_term = torch.exp(\n",
    "            torch.arange(0, hidden_size, 2) *\n",
    "            -(torch.log(torch.Tensor([10000.0])) / hidden_size)\n",
    "        )\n",
    "        pe[:, 0::2] = torch.sin(position * div_term)  # 偶数位置\n",
    "        pe[:, 1::2] = torch.cos(position * div_term)  # 奇数位置\n",
    "        return pe\n",
    "\n",
    "    def forward(self, input_ids):\n",
    "        # input_ids: [batch_size,seq_len]\n",
    "        seq_len = input_ids.shape[1]\n",
    "        assert (\n",
    "                seq_len <= self.max_length), f\"input sequence length should no more than {self.max_length} but got {seq_len}\"\n",
    "\n",
    "        position_ids = torch.arange(seq_len, dtype=torch.long, device=input_ids.device)  # 位置信息\n",
    "        # unsqueeze(0): 在第一维添加一个维度，使张量的形状从 [seq_len] 变为 [1, seq_len]\n",
    "        # expand_as(input_ids): 扩展张量的形状，使其与 input_ids 张量的形状相同\n",
    "        position_ids = position_ids.unsqueeze(0).expand_as(input_ids)\n",
    "        # position_ids: [batch_size,seq_len]\n",
    "\n",
    "        # print(input_ids.shape) #为了调试\n",
    "        # print(position_ids.shape) #为了调试\n",
    "        # print(position_ids) #为了调试\n",
    "\n",
    "        # embedding层\n",
    "        word_embeds = self.word_embedding(input_ids)  # 词嵌入\n",
    "        # word_embeds: [batch_size,seq_len,hidden_size]\n",
    "        # position_ids: [batch_size,seq_len]\n",
    "        pos_embeds = self.position_embedding(position_ids)  # 位置嵌入 # ？\n",
    "        # pos_embeds: [batch_size,seq_len,hidden_size]   \n",
    "        embeds = word_embeds + pos_embeds  # 相加得到嵌入向量\n",
    "        # embeds: [batch_size,seq_len,hidden_size]\n",
    "        embeds = self.dropout(embeds)  # 随机失活\n",
    "        return embeds\n",
    "\n",
    "\n",
    "# #随机input，调用TransformerEmbedding\n",
    "# config={\n",
    "#     \"vocab_size\": 100,\n",
    "#     \"d_model\": 128,\n",
    "#     \"pad_idx\": 0,\n",
    "#     \"max_length\": 64,\n",
    "#     \"dropout\": 0.1,\n",
    "# }\n",
    "# input_ids = torch.randint(0, 100, (2, 50))\n",
    "# embeds = TransformerEmbedding(config)(input_ids)\n",
    "# embeds.shape\n",
    "\n",
    "# 绘制位置编码\n",
    "def plot_position_embedding(position_embedding):\n",
    "    plt.pcolormesh(position_embedding)  # 绘制位置编码矩阵\n",
    "    plt.xlabel('Depth')\n",
    "    plt.ylabel('Position')\n",
    "    plt.colorbar()  # 颜色条，-1到1的颜色范围\n",
    "    plt.show()\n",
    "\n",
    "\n",
    "position_embedding = TransformerEmbedding.get_position_encoding(64, 128)\n",
    "plot_position_embedding(position_embedding)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "39630e37b910bd7f",
   "metadata": {},
   "source": [
    "## Transformer Block"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "72e7edad4645f763",
   "metadata": {},
   "source": [
    "### scaled-dot-product-attention"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "7bd08ede5d628f45",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:29.834753Z",
     "start_time": "2025-02-06T14:04:29.821751Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-06T15:33:17.447688Z",
     "iopub.status.busy": "2025-02-06T15:33:17.447461Z",
     "iopub.status.idle": "2025-02-06T15:33:17.456593Z",
     "shell.execute_reply": "2025-02-06T15:33:17.456140Z",
     "shell.execute_reply.started": "2025-02-06T15:33:17.447669Z"
    }
   },
   "outputs": [],
   "source": [
    "from dataclasses import dataclass\n",
    "from typing import Optional, Tuple\n",
    "\n",
    "Tensor = torch.Tensor\n",
    "\n",
    "\n",
    "# 修饰器 @dataclass 会自动生成 __init__、__repr__ 和 __eq__ 等方法，减少了样板代码的编写\n",
    "@dataclass\n",
    "class AttentionOutput:\n",
    "    hidden_states: Tensor\n",
    "    attn_scores: Tensor\n",
    "\n",
    "\n",
    "class MultiHeadAttention(nn.Module):\n",
    "    def __init__(self, config):\n",
    "        super().__init__()\n",
    "        # hyper params\n",
    "        self.hidden_size = config[\"d_model\"]  # 隐藏层大小\n",
    "        self.num_heads = config[\"num_heads\"]  # 多头注意力的头数\n",
    "        assert (\n",
    "                self.hidden_size % self.num_heads == 0\n",
    "        ), \"Hidden size must be divisible by num_heads but got {} and {}\".format(\n",
    "            self.hidden_size, self.num_heads)\n",
    "        self.head_dim = self.hidden_size // self.num_heads  # 每个头的维度\n",
    "\n",
    "        # layers\n",
    "        # 第二个self.hidden_size可以*系数\n",
    "        self.Wq = nn.Linear(self.hidden_size, self.hidden_size, bias=False)\n",
    "        self.Wk = nn.Linear(self.hidden_size, self.hidden_size, bias=False)\n",
    "        self.Wv = nn.Linear(self.hidden_size, self.hidden_size, bias=False)\n",
    "        self.Wo = nn.Linear(self.hidden_size, self.hidden_size, bias=False)  # 输出层\n",
    "\n",
    "    # -> Tensor 是一种类型注解，表示函数的返回值类型是 Tensor\n",
    "    def _split_heads(self, x: Tensor) -> Tensor:\n",
    "        # 若hidden_size是512\n",
    "        bs, seq_len, _ = x.shape  # [batch_size,seq_len,hidden_size]\n",
    "        # 先将x变形为[batch_size,seq_len,num_heads,head_dim]\n",
    "        x = x.view(bs, seq_len, self.num_heads, self.head_dim)\n",
    "        # num_heads是8，head_dim是64\n",
    "        return x.permute(0, 2, 1, 3)\n",
    "        # 变换维度，[batch_size, num_heads, seq_len, head_dim]\n",
    "\n",
    "    def _merge_heads(self, x: Tensor) -> Tensor:\n",
    "        # 将多头注意力的输出合并为一个张量\n",
    "        bs, _, seq_len, _ = x.shape  # [batch_size, num_heads, seq_len, head_dim]\n",
    "        # 变换维度，变为[batch_size, seq_len, hidden_size]\n",
    "        return x.permute(0, 2, 1, 3).reshape(bs, seq_len, self.hidden_size)\n",
    "\n",
    "    def forward(self, querys, keys, values, attn_mask=None) -> AttentionOutput:\n",
    "        # split heads\n",
    "        # [batch_size, seq_len,hidden_dim]->[batch_size, num_heads, seq_len, head_dim]\n",
    "        querys = self._split_heads(self.Wq(querys))\n",
    "        keys = self._split_heads(self.Wk(keys))\n",
    "        values = self._split_heads(self.Wv(values))\n",
    "\n",
    "        # calculate attention scores\n",
    "        # 计算注意力分数，matmul是矩阵乘法(在后两个维度上进行)，mT是矩阵转置,\n",
    "        # qk_logits是[batch_size, num_heads, seq_len, seq_len]\n",
    "        qk_logits = torch.matmul(querys, keys.mT)\n",
    "        if attn_mask is not None:\n",
    "            # seq_len*seq_len的部分是有效的\n",
    "            attn_mask = attn_mask[:, :, :querys.shape[-2], :keys.shape[-2]]  #切片\n",
    "            qk_logits += attn_mask * -1e9  # 给需要mask的地方设置一个负无穷\n",
    "        # 计算注意力数,softmax是在seq_len维度上进行的\n",
    "        attn_scores = F.softmax(qk_logits / (self.head_dim ** 0.5), dim=-1)\n",
    "        # attn_scores: [batch_size, num_heads, seq_len, seq_len]\n",
    "\n",
    "        # softmax后的结果与value相乘，得到新的表示\n",
    "        embeds = torch.matmul(attn_scores, values)\n",
    "        # embeds的尺寸是[batch_size, num_heads, seq_len, head_dim]\n",
    "        # _merge_heads(embeds)的输出是[batch_size, seq_len, hidden_size]\n",
    "        embeds = self.Wo(self._merge_heads(embeds))  # 输出层\n",
    "        # embeds: [batch_size, seq_len, hidden_size]\n",
    "\n",
    "        return AttentionOutput(hidden_states=embeds, attn_scores=attn_scores)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "a9eb177f33d1050d",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:29.843757Z",
     "start_time": "2025-02-06T14:04:29.835755Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-06T15:33:17.457340Z",
     "iopub.status.busy": "2025-02-06T15:33:17.457093Z",
     "iopub.status.idle": "2025-02-06T15:33:17.463569Z",
     "shell.execute_reply": "2025-02-06T15:33:17.463060Z",
     "shell.execute_reply.started": "2025-02-06T15:33:17.457324Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "key_value.shape torch.Size([2, 4, 2])\n",
      "torch.Size([2, 3, 2])\n",
      "torch.Size([2, 2, 3, 4])\n"
     ]
    }
   ],
   "source": [
    "mha = MultiHeadAttention({\"num_heads\": 2, \"d_model\": 2})\n",
    "query = torch.randn(2, 3, 2)  # [batch_size, seq_len, hidden_size]\n",
    "query /= query.norm(dim=-1, keepdim=True)  # 归一化\n",
    "key_value = torch.randn(2, 4, 2)\n",
    "print(f'key_value.shape {key_value.shape}')\n",
    "outputs = mha(query, key_value, key_value)  #最终输出shape和query的shape一样\n",
    "print(outputs.hidden_states.shape)\n",
    "print(outputs.attn_scores.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "e37f29d98cf51bac",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:29.852319Z",
     "start_time": "2025-02-06T14:04:29.845757Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-06T15:33:17.464641Z",
     "iopub.status.busy": "2025-02-06T15:33:17.464187Z",
     "iopub.status.idle": "2025-02-06T15:33:17.469811Z",
     "shell.execute_reply": "2025-02-06T15:33:17.469388Z",
     "shell.execute_reply.started": "2025-02-06T15:33:17.464612Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[0.2254, 0.2314, 0.5431],\n",
      "        [0.6427, 0.1704, 0.1869]])\n"
     ]
    }
   ],
   "source": [
    "#随机一个2阶张量，在-1维度计算softmax\n",
    "import torch.nn.functional as F\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "x = torch.randn(2, 3)\n",
    "x_softmax = F.softmax(x, dim=-1)\n",
    "print(x_softmax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "b451e28eb10aaf26",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:30.232694Z",
     "start_time": "2025-02-06T14:04:30.002057Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-06T15:33:17.470399Z",
     "iopub.status.busy": "2025-02-06T15:33:17.470251Z",
     "iopub.status.idle": "2025-02-06T15:33:17.741977Z",
     "shell.execute_reply": "2025-02-06T15:33:17.741498Z",
     "shell.execute_reply.started": "2025-02-06T15:33:17.470384Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plt.subplots() 用于创建子图网格，其维度基于 outputs.attn_scores.shape[:2]。子图的行数和列数似乎由 outputs.attn_scores 的前两个维度确定。\n",
    "fig, axis = plt.subplots(*outputs.attn_scores.shape[:2])\n",
    "for i in range(query.shape[0]):\n",
    "    for j in range(outputs.attn_scores.shape[1]):\n",
    "        # axis[i, j].matshow(outputs.attn_scores[i, j].detach().numpy())：此行使用 Matplotlib 的 matshow 绘制每个 i 和 j 的注意力分数热图。detach().numpy() 将 PyTorch 张量转换为 NumPy 数组以进行可视化。\n",
    "        axis[i, j].matshow(outputs.attn_scores[i, j].detach().numpy())\n",
    "        for x in range(outputs.attn_scores.shape[2]):\n",
    "            for y in range(outputs.attn_scores.shape[3]):\n",
    "                # axis[i, j].text(y, x, f\"{outputs.attn_scores[i, j, x, y]:.2f}\", ha=\"center\", va=\"center\", color=\"w\")：此代码在热图上叠加文本，显示 (x, y) 位置处的注意力分数。格式化部分 f\"{outputs.attn_scores[i, j, x, y]:.2f}\" 确保以两位小数显示注意力分数。文本以白色居中显示在 (y, x) 坐标处。\n",
    "                axis[i, j].text(y, x, f\"{outputs.attn_scores[i, j, x, y]:.2f}\", ha=\"center\", va=\"center\", color=\"w\")\n",
    "fig.suptitle(\"multi head attention without mask\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "42dc1608bbcd426c",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:30.480546Z",
     "start_time": "2025-02-06T14:04:30.233690Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-06T15:33:17.742753Z",
     "iopub.status.busy": "2025-02-06T15:33:17.742524Z",
     "iopub.status.idle": "2025-02-06T15:33:17.946167Z",
     "shell.execute_reply": "2025-02-06T15:33:17.945696Z",
     "shell.execute_reply.started": "2025-02-06T15:33:17.742735Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# mask\n",
    "mask = torch.Tensor([[0, 0, 1, 1], [0, 0, 0, 1], [0, 0, 0, 0]]).reshape(1, 1, 3, 4)  #手工构造mask\n",
    "outputs_masked = mha(query, key_value, key_value, mask)\n",
    "\n",
    "fig, axis = plt.subplots(*outputs_masked.attn_scores.shape[:2])\n",
    "for i in range(query.shape[0]):\n",
    "    for j in range(outputs_masked.attn_scores.shape[1]):\n",
    "        axis[i, j].matshow(outputs_masked.attn_scores[i, j].detach().numpy())\n",
    "        for x in range(outputs_masked.attn_scores.shape[2]):\n",
    "            for y in range(outputs_masked.attn_scores.shape[3]):\n",
    "                axis[i, j].text(y, x, f\"{outputs_masked.attn_scores[i, j, x, y]:.2f}\", ha=\"center\", va=\"center\",\n",
    "                                color=\"w\")\n",
    "fig.suptitle(\"multi head attention with mask\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fd2b7a6a479f877c",
   "metadata": {},
   "source": [
    "### Transformer-Block"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "2247e4861550df31",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:30.489814Z",
     "start_time": "2025-02-06T14:04:30.481541Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-06T15:33:17.946963Z",
     "iopub.status.busy": "2025-02-06T15:33:17.946729Z",
     "iopub.status.idle": "2025-02-06T15:33:17.955396Z",
     "shell.execute_reply": "2025-02-06T15:33:17.954907Z",
     "shell.execute_reply.started": "2025-02-06T15:33:17.946945Z"
    }
   },
   "outputs": [],
   "source": [
    "@dataclass\n",
    "class TransformerBlockOutput:\n",
    "    # 用于存储某个块产生的隐藏状态。\n",
    "    hidden_states: Tensor\n",
    "    # 包含了自注意力机制（self-attention）所计算得到的注意力分数\n",
    "    self_attn_scores: Tensor\n",
    "    # 是一个可选字段，存储了交叉注意力（cross-attention）计算得到的注意力分数。\n",
    "    # 这里的 Optional 表示这个字段可以是 Tensor 类型，也可以是 None。\n",
    "    cross_attn_scores: Optional[Tensor] = None\n",
    "\n",
    "\n",
    "class TransformerBlock(nn.Module):\n",
    "    def __init__(self, config, add_cross_attention=False):\n",
    "        super().__init__()\n",
    "        # hyper params\n",
    "        self.hidden_size = config[\"d_model\"]  # 隐藏层大小\n",
    "        self.num_heads = config[\"num_heads\"]  # 多头注意力的头数\n",
    "        dropout_rate = config[\"dropout\"]  # 随机失活率\n",
    "        ffn_dim = config[\"dim_feedforward\"]  # 前馈网络的维度\n",
    "        eps = config[\"layer_norm_eps\"]  # 层归一化的epsilon值\n",
    "\n",
    "        # self-attention\n",
    "        self.self_attn = MultiHeadAttention(config)  # 多头注意力\n",
    "        self.self_ln = nn.LayerNorm(self.hidden_size, eps=eps)  # 层归一化(层标准化)\n",
    "        self.self_dropout = nn.Dropout(dropout_rate)  # 随机失活\n",
    "\n",
    "        # cross-attention，交叉注意力，decoder中使用,因此额外做一个判断\n",
    "        if add_cross_attention:\n",
    "            self.cross_attn = MultiHeadAttention(config)\n",
    "            self.cross_ln = nn.LayerNorm(self.hidden_size, eps=eps)\n",
    "            self.cross_dropout = nn.Dropout(dropout_rate)\n",
    "        else:\n",
    "            self.cross_attn = None\n",
    "\n",
    "        # FFN,前馈神经网络\n",
    "        self.ffn = nn.Sequential(\n",
    "            nn.Linear(self.hidden_size, ffn_dim),\n",
    "            nn.ReLU(),\n",
    "            nn.Linear(ffn_dim, self.hidden_size),\n",
    "        )\n",
    "        self.ffn_ln = nn.LayerNorm(self.hidden_size, eps=eps)\n",
    "        self.ffn_dropout = nn.Dropout(dropout_rate)\n",
    "\n",
    "    def forward(\n",
    "            self,\n",
    "            hidden_states,\n",
    "            attn_mask=None,\n",
    "            encoder_outputs=None,\n",
    "            cross_attn_mask=None,\n",
    "    ):\n",
    "        # self-attention,自注意力\n",
    "        self_attn_outputs = self.self_attn(\n",
    "            hidden_states, hidden_states, hidden_states, attn_mask\n",
    "        )  # self_attn_outputs 是 AttentionOutput 类型\n",
    "        self_embeds = self.self_ln(\n",
    "            hidden_states + self.self_dropout(self_attn_outputs.hidden_states)\n",
    "        )  #多头注意力进行dropout，然后和原始输入进行残差连接，然后进行层归一化\n",
    "\n",
    "        # cross-attention，交叉注意力\n",
    "        if self.cross_attn is not None:\n",
    "            assert encoder_outputs is not None, \"encoder_outputs should not be None\"\n",
    "            cross_attn_outputs = self.cross_attn(\n",
    "                self_embeds, encoder_outputs, encoder_outputs, cross_attn_mask\n",
    "            )  # query是self_embeds，key和value都是encoder_outputs\n",
    "            cross_embeds = self.cross_ln(\n",
    "                self_embeds + self.cross_dropout(cross_attn_outputs.hidden_states)\n",
    "            )  # 交叉注意力进行dropout，然后和self_embeds进行残差连接，然后进行层归一化\n",
    "\n",
    "        # FFN\n",
    "        # 如果有交叉注意力，则使用交叉注意力的输出作为FFN的输入；否则，使用self_embeds作为FFN的输入\n",
    "        embeds = cross_embeds if self.cross_attn is not None else self_embeds\n",
    "        # embeds:[batch_size, seq_len, hidden_size]\n",
    "        ffn_output = self.ffn(embeds)  # 前馈神经网络\n",
    "        # ffn_output:[batch_size, seq_len, hidden_size]\n",
    "        # 前馈神经网络进行dropout，然后和原始输入进行残差连接，然后进行层归一化\n",
    "        embeds = self.ffn_ln(embeds + self.ffn_dropout(ffn_output))\n",
    "\n",
    "        return TransformerBlockOutput(\n",
    "            hidden_states=embeds,\n",
    "            self_attn_scores=self_attn_outputs.attn_scores,\n",
    "            cross_attn_scores=cross_attn_outputs.attn_scores if self.cross_attn is not None else None,\n",
    "        )\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "19be62a364cb5cc2",
   "metadata": {},
   "source": [
    "## Encoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "4f0e09edb3e7bbfe",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:30.497908Z",
     "start_time": "2025-02-06T14:04:30.490811Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-06T15:33:17.956327Z",
     "iopub.status.busy": "2025-02-06T15:33:17.955948Z",
     "iopub.status.idle": "2025-02-06T15:33:17.961058Z",
     "shell.execute_reply": "2025-02-06T15:33:17.960555Z",
     "shell.execute_reply.started": "2025-02-06T15:33:17.956309Z"
    }
   },
   "outputs": [],
   "source": [
    "from typing import List\n",
    "\n",
    "\n",
    "@dataclass\n",
    "class TransformerEncoderOutput:\n",
    "    last_hidden_states: Tensor\n",
    "    attn_scores: List[Tensor]\n",
    "\n",
    "\n",
    "class TransformerEncoder(nn.Module):\n",
    "    def __init__(self, config):\n",
    "        super().__init__()\n",
    "        # hyper params\n",
    "        self.num_layers = config[\"num_encoder_layers\"]\n",
    "\n",
    "        # layers,仅仅是一个模块的列表，它本身没有定义前向传递（forward pass）过程。你需要在 forward 方法中明确地定义如何使用这些模块。\n",
    "        self.layers = nn.ModuleList(\n",
    "            [TransformerBlock(config) for _ in range(self.num_layers)]\n",
    "        )\n",
    "\n",
    "    def forward(\n",
    "            self, encoder_inputs_embeds, attn_mask=None\n",
    "    ) -> TransformerEncoderOutput:\n",
    "        # 存储每个层的注意力分数\n",
    "        attn_scores = []\n",
    "        # 输入的嵌入向量作为第一层的输入(embedding+位置编码)\n",
    "        embeds = encoder_inputs_embeds\n",
    "        for layer in self.layers:\n",
    "            # layer就是一个TransformerBlock(config)\n",
    "            block_outputs = layer(embeds, attn_mask=attn_mask)\n",
    "            # 在每个层的输出中，提取了隐藏状态 block_outputs.hidden_states，\n",
    "            embeds = block_outputs.hidden_states  # 上一层的输出作为下一层的输入\n",
    "            # 并将对应的注意力分数 block_outputs.self_attn_scores 添加到列表 attn_scores 中。\n",
    "            attn_scores.append(block_outputs.self_attn_scores)  # 存储每个层的注意力分数,用于画图\n",
    "\n",
    "        return TransformerEncoderOutput(\n",
    "            last_hidden_states=embeds, attn_scores=attn_scores\n",
    "        )\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a142b31a53d27997",
   "metadata": {},
   "source": [
    "## Decoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "c0e7f64561cbd5f",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:30.505801Z",
     "start_time": "2025-02-06T14:04:30.498905Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-06T15:33:17.961705Z",
     "iopub.status.busy": "2025-02-06T15:33:17.961552Z",
     "iopub.status.idle": "2025-02-06T15:33:17.967190Z",
     "shell.execute_reply": "2025-02-06T15:33:17.966500Z",
     "shell.execute_reply.started": "2025-02-06T15:33:17.961690Z"
    }
   },
   "outputs": [],
   "source": [
    "@dataclass\n",
    "class TransformerDecoderOutput:\n",
    "    last_hidden_states: Tensor\n",
    "    self_attn_scores: List[Tensor]\n",
    "    cross_attn_scores: List[Tensor]\n",
    "\n",
    "\n",
    "class TransformerDecoder(nn.Module):\n",
    "    def __init__(self, config):\n",
    "        super().__init__()\n",
    "        # hyper params\n",
    "        self.num_layers = config[\"num_decoder_layers\"]\n",
    "\n",
    "        # layers\n",
    "        self.layers = nn.ModuleList(\n",
    "            [\n",
    "                TransformerBlock(config, add_cross_attention=True)\n",
    "                for _ in range(self.num_layers)\n",
    "            ]\n",
    "        )\n",
    "\n",
    "    def forward(\n",
    "            self,\n",
    "            decoder_inputs_embeds,\n",
    "            encoder_outputs,\n",
    "            attn_mask=None,\n",
    "            cross_attn_mask=None,\n",
    "    ) -> TransformerDecoderOutput:\n",
    "        self_attn_scores = []  # 存储每个层的自注意力分数\n",
    "        cross_attn_scores = []  # 存储每个层的交叉注意力分数\n",
    "        # 输入的嵌入向量作为第一层的输入(embedding+位置编码)\n",
    "        embeds = decoder_inputs_embeds\n",
    "        for layer in self.layers:\n",
    "            # layer就是一个TransformerBlock(config, add_cross_attention=True)\n",
    "            # 为什么交叉注意力的mask要传入cross_attn_mask而不是attn_mask？\n",
    "            # 因为计算MutilHeadAttention的时候,keys和values都是encoder_outputs，query是decoder的输出，因此需要传入cross_attn_mask。\n",
    "            block_outputs = layer(\n",
    "                embeds,\n",
    "                attn_mask=attn_mask,  # 自注意力的mask\n",
    "                encoder_outputs=encoder_outputs,\n",
    "                cross_attn_mask=cross_attn_mask,  # 交叉注意力的mask\n",
    "            )\n",
    "            embeds = block_outputs.hidden_states  # 上一层的输出作为下一层的输入\n",
    "            self_attn_scores.append(block_outputs.self_attn_scores)  # 存储每个层的自注意力分数\n",
    "            cross_attn_scores.append(block_outputs.cross_attn_scores)  # 存储每个层的交叉注意力分数\n",
    "\n",
    "        return TransformerDecoderOutput(\n",
    "            last_hidden_states=embeds,\n",
    "            self_attn_scores=self_attn_scores,\n",
    "            cross_attn_scores=cross_attn_scores,\n",
    "        )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1c345d9c0e326a13",
   "metadata": {},
   "source": [
    "#### mask\n",
    "\n",
    "- mask实际上大类上只有两种\n",
    "    1. `padding_mask`：mask掉`pad_idx`，不计算损失\n",
    "    2. `attention_mask`：mask掉`pad_idx`，不计算注意力分数\n",
    "- Decoder的`attention_mask`和Encoder有一定的区别：\n",
    "    - Encoder可以同时看见序列所有信息，故只mask掉`pad_idx`\n",
    "    - Decoder只能看到在自身之前的序列的信息，故要额外mask掉自身之后的序列"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "9baa438dbfbcdd8a",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:30.512835Z",
     "start_time": "2025-02-06T14:04:30.506798Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-06T15:33:17.971246Z",
     "iopub.status.busy": "2025-02-06T15:33:17.970806Z",
     "iopub.status.idle": "2025-02-06T15:33:17.980254Z",
     "shell.execute_reply": "2025-02-06T15:33:17.979772Z",
     "shell.execute_reply.started": "2025-02-06T15:33:17.971222Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[False, False, False, False, False],\n",
      "        [ True, False, False, False, False],\n",
      "        [ True,  True, False, False, False],\n",
      "        [ True,  True,  True, False, False],\n",
      "        [ True,  True,  True,  True, False]])\n",
      "--------------------------------------------------\n",
      "tensor([[False,  True,  True,  True,  True],\n",
      "        [False, False,  True,  True,  True],\n",
      "        [False, False, False,  True,  True],\n",
      "        [False, False, False, False,  True],\n",
      "        [False, False, False, False, False]])\n"
     ]
    }
   ],
   "source": [
    "print((torch.triu(torch.ones(5, 5)) == 0))\n",
    "print(\"-\" * 50)\n",
    "# 符合Decoder的attention_mask形式\n",
    "print((torch.triu(torch.ones(5, 5)) == 0).transpose(-1, -2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "52343e686b4eed69",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:30.667599Z",
     "start_time": "2025-02-06T14:04:30.513830Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-06T15:33:17.981018Z",
     "iopub.status.busy": "2025-02-06T15:33:17.980716Z",
     "iopub.status.idle": "2025-02-06T15:33:18.108148Z",
     "shell.execute_reply": "2025-02-06T15:33:18.107725Z",
     "shell.execute_reply.started": "2025-02-06T15:33:17.981002Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 480x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def generate_square_subsequent_mask(sz: int) -> Tensor:\n",
    "    # The masked positions are filled with True.\n",
    "    # Unmasked positions are filled with False.\n",
    "    # torch.ones(sz, sz): 创建一个全为 1 的 sz × sz 的矩阵。\n",
    "    # torch.triu(...): 使用 triu 函数取得矩阵的上三角部分，将主对角线以下部分置零。\n",
    "    mask = (torch.triu(torch.ones(sz, sz)) == 0).transpose(-1, -2).bool()\n",
    "    return mask\n",
    "\n",
    "\n",
    "#画出一个16×16的矩阵热力图，黄色部分为True，是掩码\n",
    "plt.matshow(generate_square_subsequent_mask(16))\n",
    "plt.colorbar()\n",
    "plt.xlabel(\"keys\")\n",
    "plt.ylabel(\"querys\")\n",
    "plt.title(\"1 means mask while 0 means unmask\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "2548d49c0f7a8fd5",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:31.199068Z",
     "start_time": "2025-02-06T14:04:30.668599Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-06T15:33:18.108994Z",
     "iopub.status.busy": "2025-02-06T15:33:18.108654Z",
     "iopub.status.idle": "2025-02-06T15:33:18.518219Z",
     "shell.execute_reply": "2025-02-06T15:33:18.517787Z",
     "shell.execute_reply.started": "2025-02-06T15:33:18.108977Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['[BOS]', '[UNK]', 'quick', 'brown', '[UNK]', 'jumps', 'over', 'the', '[UNK]', 'dog', '.', '[EOS]']\n",
      "--------------------------------------------------\n",
      "tensor([[0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.]], dtype=torch.float64)\n",
      "--------------------------------------------------\n",
      "tensor([[0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.]], dtype=torch.float64)\n",
      "--------------------------------------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------------------\n",
      "['[BOS]', '[UNK]', 'does', 'the', '[UNK]', 'say', '?', '[EOS]', '[PAD]', '[PAD]', '[PAD]', '[PAD]']\n",
      "--------------------------------------------------\n",
      "tensor([[0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1., 1.]], dtype=torch.float64)\n",
      "--------------------------------------------------\n",
      "tensor([[0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1., 1.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1., 1.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1., 1.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1., 1.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1., 1.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1., 1.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1., 1.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1., 1.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1., 1.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1., 1.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1., 1.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1., 1.]], dtype=torch.float64)\n",
      "--------------------------------------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "#通过下面代码查看mask的效果\n",
    "inputs_words = [\"The quick brown fox jumps over the lazy dog .\", \"What does the fox say ?\"]\n",
    "\n",
    "inputs_ids, inputs_mask = tokenizer.encode([w.split() for w in inputs_words], return_mask=True)\n",
    "for i in range(len(inputs_ids)):\n",
    "    decode_text = \\\n",
    "        tokenizer.decode(inputs_ids[i:i + 1].tolist(), remove_bos=False, remove_eos=False, remove_pad=False,\n",
    "                         split=True)[0]\n",
    "    print(decode_text)\n",
    "    print(\"-\" * 50)\n",
    "\n",
    "    print(inputs_mask[i].reshape(1, -1))\n",
    "    print(\"-\" * 50)\n",
    "    # reshape(1, -1):将 input_mask[i] 的形状调整为 (1, sequence_length)\n",
    "    # repeat_interleave 在第 0 维（dim=0）上重复 sequence_length 次，生成形状为 (sequence_length, sequence_length) 的掩码。\n",
    "    self_attn_mask = inputs_mask[i].reshape(1, -1).repeat_interleave(inputs_ids.shape[-1], dim=0)\n",
    "    print(self_attn_mask)\n",
    "    print(\"-\" * 50)\n",
    "\n",
    "    look_ahead_mask = generate_square_subsequent_mask(inputs_ids.shape[-1])\n",
    "\n",
    "    fig, axs = plt.subplots(1, 2, figsize=(10, 5))\n",
    "    axs[0].matshow(self_attn_mask)\n",
    "    axs[0].set_title(\"self_attn_mask\")\n",
    "    axs[0].set_yticks(range(len(decode_text)), decode_text, fontsize=6)\n",
    "    axs[0].set_ylabel(\"querys\")\n",
    "    axs[0].set_xticks(range(len(decode_text)), decode_text, fontsize=6)\n",
    "    axs[0].set_xlabel(\"keys\")\n",
    "    axs[1].matshow(look_ahead_mask)\n",
    "    axs[1].set_title(\"look_ahead_mask\")\n",
    "    axs[1].set_yticks(range(len(decode_text)), decode_text, fontsize=6)\n",
    "    axs[1].set_ylabel(\"querys\")\n",
    "    axs[1].set_xticks(range(len(decode_text)), decode_text, fontsize=6)\n",
    "    axs[1].set_xlabel(\"keys\")\n",
    "    plt.show()\n",
    "    print('-' * 50)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "14c5b12ee51329e8",
   "metadata": {},
   "source": [
    "## Transformer Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "89a32ab3ca75bf0c",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:31.215535Z",
     "start_time": "2025-02-06T14:04:31.200067Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-06T15:33:18.519184Z",
     "iopub.status.busy": "2025-02-06T15:33:18.518821Z",
     "iopub.status.idle": "2025-02-06T15:33:18.533770Z",
     "shell.execute_reply": "2025-02-06T15:33:18.533330Z",
     "shell.execute_reply.started": "2025-02-06T15:33:18.519165Z"
    }
   },
   "outputs": [],
   "source": [
    "@dataclass\n",
    "class TransformerOutput:\n",
    "    logits: Tensor\n",
    "    encoder_last_hidden_states: Tensor\n",
    "    encoder_attn_scores: List[Tensor]  #画图\n",
    "    decoder_last_hidden_states: Tensor\n",
    "    decoder_self_attn_scores: List[Tensor]  #画图\n",
    "    decoder_cross_attn_scores: List[Tensor]  #画图\n",
    "    preds: Optional[Tensor] = None\n",
    "\n",
    "\n",
    "class TransformerModel(nn.Module):\n",
    "    def __init__(self, config):\n",
    "        super().__init__()\n",
    "        # hyper params\n",
    "        self.hidden_size = config[\"d_model\"]\n",
    "        self.num_encoder_layers = config[\"num_encoder_layers\"]\n",
    "        self.num_decoder_layers = config[\"num_decoder_layers\"]\n",
    "        self.pad_idx = config[\"pad_idx\"]\n",
    "        self.bos_idx = config[\"bos_idx\"]\n",
    "        self.eos_idx = config[\"eos_idx\"]\n",
    "        self.vocab_size = config[\"vocab_size\"]\n",
    "        self.dropout_rate = config[\"dropout\"]\n",
    "        self.max_length = config[\"max_length\"]\n",
    "        # 是否共享词嵌入，\n",
    "        # 共享后，decoder的词嵌入层和encoder的词嵌入层相同，节省内存\n",
    "        self.share = config[\"share_embedding\"]\n",
    "\n",
    "        # layers\n",
    "        self.src_embedding = TransformerEmbedding(config)  # 输入的嵌入层\n",
    "        # 如果共享词嵌入，则使用src_embedding作为trg_embedding\n",
    "        if self.share:\n",
    "            # 源和目标的嵌入层相同，共享参数，节省内存\n",
    "            self.trg_embedding = self.src_embedding\n",
    "            self.linear = lambda x: torch.matmul(\n",
    "                # x是该层的输入，[batch_size, seq_len, hidden_size]\n",
    "                # self.trg_embedding.get_word_embedding_weight().T: [hidden_size,vocab_size]\n",
    "                x, self.trg_embedding.get_word_embedding_weight().T\n",
    "            )  # 输出层，共享参数，直接拿原有embedding矩阵的转置，节省内存\n",
    "        else:\n",
    "            self.trg_embedding = TransformerEmbedding(config)  # decoder模块的嵌入层\n",
    "            self.linear = nn.Linear(self.hidden_size, self.vocab_size)  # 输出层\n",
    "\n",
    "        self.encoder = TransformerEncoder(config)\n",
    "        self.decoder = TransformerDecoder(config)\n",
    "\n",
    "        self._init_weights()  # init weights\n",
    "\n",
    "    def _init_weights(self):\n",
    "        # self.parameters(): 这个方法返回模型中的所有可学习参数（权重和偏置）。\n",
    "        for p in self.parameters():\n",
    "            if p.dim() > 1:  # 如果参数是二维或更高维（通常是权重矩阵），则执行初始化。\n",
    "                nn.init.xavier_uniform_(p)  # 使用 xavier 均匀分布来初始化权重\n",
    "\n",
    "    def generate_square_subsequent_mask(self, sz: int) -> Tensor:\n",
    "        # 为了生成斜三角的mask\n",
    "        mask = (torch.triu(torch.ones(sz, sz)) == 0).transpose(-1, -2).bool()\n",
    "        return mask\n",
    "\n",
    "    # 用于训练,在TransformerBlock中的每一层内是并行的，层间是串行的\n",
    "    def forward(\n",
    "            self, encoder_inputs, decoder_inputs, encoder_inputs_mask=None\n",
    "    ) -> TransformerOutput:\n",
    "        # encoder_inputs: [batch_size, src_len]\n",
    "        # decoder_inputs: [batch_size, trg_len]\n",
    "        # encoder_inputs_mask: [batch_size, src_len]\n",
    "        if encoder_inputs_mask is None:\n",
    "            # 返回一个布尔张量，形状与 encoder_inputs 相同。\n",
    "            # 该布尔张量的每个位置会根据 encoder_inputs 中的值是否等于 self.pad_idx 来设置：\n",
    "            # 如果相等，值为 True（表示该位置是填充位置）。\n",
    "            # 如果不相等，值为 False（表示该位置是有效输入） \n",
    "            encoder_inputs_mask = encoder_inputs.eq(self.pad_idx)\n",
    "            # encoder_inputs_mask: [batch_size, src_len]\n",
    "        # unsqueeze(1): 这个操作在张量的第 1 个维度上增加一个新的维度\n",
    "        #    得到 (batch_size, 1, src_len)。\n",
    "        # unsqueeze(2): 这个操作在张量的第 2 个维度上再次增加一个新的维度。\n",
    "        #    得到 (batch_size, 1, 1, src_len)。\n",
    "        encoder_inputs_mask = encoder_inputs_mask.unsqueeze(1).unsqueeze(2)\n",
    "        # [batch_size, 1, 1, src_len],用于encoder的自注意力\n",
    "\n",
    "        look_ahead_mask = self.generate_square_subsequent_mask(decoder_inputs.shape[-1])  # [trg_len, trg_len]\n",
    "        look_ahead_mask = (\n",
    "            look_ahead_mask.unsqueeze(0).unsqueeze(0).to(decoder_inputs.device)\n",
    "        )  # [1, 1, trg_len, trg_len] 用于decoder的自注意力\n",
    "\n",
    "        # 增加decoder_inputs_mask和look_ahead_mask进行组合\n",
    "        decoder_inputs_mask = decoder_inputs.eq(self.pad_idx)\n",
    "        # [batch_size, trg_len]，和上面encoder_inputs_mask一致\n",
    "        decoder_inputs_mask = decoder_inputs_mask.unsqueeze(1).unsqueeze(2)\n",
    "        # [batch_size, 1, 1, trg_len]\n",
    "        decoder_inputs_mask = decoder_inputs_mask + look_ahead_mask\n",
    "        # [batch_size, 1, 1, trg_len]与[1, 1, trg_len, trg_len]相加，得到decoder的自注意力mask\n",
    "        # decoder_inputs_mask : [batch_size, 1, trg_len,trg_len]\n",
    "\n",
    "        # encoding\n",
    "        # src_embedding是TransformerEmbedding(config)\n",
    "        encoder_inputs_embeds = self.src_embedding(encoder_inputs)\n",
    "        # encoder是TransformerEncoder(config)\n",
    "        encoder_outputs = self.encoder(\n",
    "            encoder_inputs_embeds, attn_mask=encoder_inputs_mask\n",
    "        )  # encoder_inputs_mask用于encoder的自注意力,广播去做计算 \n",
    "\n",
    "        # decoding\n",
    "        # decoder_inputs_embeds是TransformerEmbedding(config)\n",
    "        decoder_inputs_embeds = self.trg_embedding(decoder_inputs)\n",
    "        # decoder是TransformerDecoder(config)\n",
    "        decoder_outputs = self.decoder(\n",
    "            decoder_inputs_embeds=decoder_inputs_embeds,\n",
    "            encoder_outputs=encoder_outputs.last_hidden_states,\n",
    "            attn_mask=decoder_inputs_mask,  # 用于decoder的自注意力,广播去做计算\n",
    "            cross_attn_mask=encoder_inputs_mask,  # 用于decoder的交叉注意力,广播去做计算\n",
    "        )\n",
    "        # decoder_outputs.last_hidden_states: [batch_size, trg_len, hidden_size]\n",
    "        logits = self.linear(decoder_outputs.last_hidden_states)\n",
    "        # [batch_size, trg_len, vocab_size]\n",
    "\n",
    "        return TransformerOutput(\n",
    "            logits=logits,\n",
    "            encoder_last_hidden_states=encoder_outputs.last_hidden_states,\n",
    "            encoder_attn_scores=encoder_outputs.attn_scores,\n",
    "            decoder_last_hidden_states=decoder_outputs.last_hidden_states,\n",
    "            decoder_self_attn_scores=decoder_outputs.self_attn_scores,\n",
    "            decoder_cross_attn_scores=decoder_outputs.cross_attn_scores,\n",
    "        )\n",
    "\n",
    "    @torch.no_grad()  # 禁用梯度计算\n",
    "    def infer(self, encoder_inputs, encoder_inputs_mask=None) -> TransformerOutput:\n",
    "        # 应对多个样本同时进行推理\n",
    "        if encoder_inputs_mask is None:\n",
    "            encoder_inputs_mask = encoder_inputs.eq(self.pad_idx)\n",
    "            # encoder_inputs_mask: [batch_size, src_len]\n",
    "        encoder_inputs_mask = encoder_inputs_mask.unsqueeze(1).unsqueeze(2)\n",
    "        # [batch_size, 1, 1, src_len],用于encoder的自注意力\n",
    "\n",
    "        look_ahead_mask = self.generate_square_subsequent_mask(self.max_length)\n",
    "        # [max_length, max_length]\n",
    "        look_ahead_mask = (\n",
    "            look_ahead_mask.unsqueeze(0).unsqueeze(0).to(encoder_inputs.device)\n",
    "        )  # [1, 1, max_length, max_length] 用于decoder的自注意力\n",
    "\n",
    "        # encoding\n",
    "        # src_embedding是TransformerEmbedding(config)\n",
    "        encoder_inputs_embeds = self.src_embedding(encoder_inputs)\n",
    "        # encoder是TransformerEncoder(config)\n",
    "        # 因为只支持单样本预测，没有paddings，所以不需要mask\n",
    "        encoder_outputs = self.encoder(encoder_inputs_embeds)\n",
    "        # encoder_outputs.last_hidden_states: [batch_size, src_len, hidden_size]\n",
    "\n",
    "        # decoding,多样本推理\n",
    "        # encoder_inputs.shape[0]: 获取编码器输入的批量大小（batch size），即样本的数量。\n",
    "        # .reshape(-1, 1): 这个操作将张量的形状调整为 (batch_size, 1)，\n",
    "        decoder_inputs = torch.Tensor([self.bos_idx] * encoder_inputs.shape[0]).reshape(-1, 1).long().to(\n",
    "            device=encoder_inputs.device)\n",
    "        # 推理是串行的，每次只推理一个token，所以需要初始化decoder_inputs为bos_idx\n",
    "        for cur_len in tqdm(range(1, self.max_length + 1)):\n",
    "            decoder_inputs_embeds = self.trg_embedding(decoder_inputs)\n",
    "            decoder_outputs = self.decoder(\n",
    "                decoder_inputs_embeds=decoder_inputs_embeds,\n",
    "                encoder_outputs=encoder_outputs.last_hidden_states,\n",
    "                # decoder的自注意力mask\n",
    "                # 因为是串行的，所以每次只看前面cur_len个token\n",
    "                attn_mask=look_ahead_mask[:, :, :cur_len, :cur_len],\n",
    "            )\n",
    "            # decoder_outputs.last_hidden_states: [batch_size, cur_len, hidden_size]\n",
    "            logits = self.linear(decoder_outputs.last_hidden_states)\n",
    "            # logits: [batch_size, cur_len, vocab_size]\n",
    "            # 通过最大下标确定类别，[:, -1:]表示取最后一个结果\n",
    "            next_token = logits.argmax(dim=-1)[:, -1:]  # [batch_size, 1]\n",
    "            # 预测输出拼接到输入中\n",
    "            decoder_inputs = torch.cat([decoder_inputs, next_token], dim=-1)\n",
    "            # dcoder_inputs: [batch_size, cur_len+1]\n",
    "\n",
    "            # (decoder_inputs == self.eos_idx).sum(dim=-1)是判断样本中是否含有EOS标记\n",
    "            # all是每一个都为True，才会结束\n",
    "            if all((decoder_inputs == self.eos_idx).sum(dim=-1) > 0):\n",
    "                break\n",
    "\n",
    "        return TransformerOutput(\n",
    "            preds=decoder_inputs[:, 1:],  # 去掉bos_idx\n",
    "            logits=logits,\n",
    "            encoder_last_hidden_states=encoder_outputs.last_hidden_states,\n",
    "            encoder_attn_scores=encoder_outputs.attn_scores,\n",
    "            decoder_last_hidden_states=decoder_outputs.last_hidden_states,\n",
    "            decoder_self_attn_scores=decoder_outputs.self_attn_scores,\n",
    "            decoder_cross_attn_scores=decoder_outputs.cross_attn_scores,\n",
    "        )\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2a740def7afa83f9",
   "metadata": {},
   "source": [
    "# 训练"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "18175b791877293",
   "metadata": {},
   "source": [
    "## 损失函数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "a2b1b4622966eb25",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:31.222855Z",
     "start_time": "2025-02-06T14:04:31.217548Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-06T15:33:18.534432Z",
     "iopub.status.busy": "2025-02-06T15:33:18.534270Z",
     "iopub.status.idle": "2025-02-06T15:33:18.538331Z",
     "shell.execute_reply": "2025-02-06T15:33:18.537918Z",
     "shell.execute_reply.started": "2025-02-06T15:33:18.534416Z"
    }
   },
   "outputs": [],
   "source": [
    "class CrossEntropyWithPadding:\n",
    "    def __init__(self, config):\n",
    "        self.label_smoothing = config[\"label_smoothing\"]\n",
    "\n",
    "    def __call__(self, logits, labels, padding_mask=None):\n",
    "        # logits: [batch_size, seq_len, vocab_size]\n",
    "        # labels: [batch_size, seq_len]\n",
    "        # padding_mask: [batch_size, seq_len]\n",
    "        bs, seq_len, nc = logits.shape\n",
    "        loss = F.cross_entropy(\n",
    "            logits.reshape(bs * seq_len, nc),\n",
    "            labels.reshape(-1),\n",
    "            reduction='none',  # 不对损失进行归约（reduction\n",
    "            label_smoothing=self.label_smoothing\n",
    "        )  # label_smoothing表示随机将一个类别的概率设置为0.1，使得模型更加关注其他类别\n",
    "        # loss: [batch_size * seq_len, 1]\n",
    "\n",
    "        if padding_mask is None:\n",
    "            loss = loss.mean()  # 计算平均损失\n",
    "        else:\n",
    "            # 将padding_mask reshape成一维张量，mask部分为0，非mask部分为1\n",
    "            padding_mask = 1 - padding_mask.reshape(-1)\n",
    "            loss = torch.mul(loss, padding_mask).sum() / padding_mask.sum()\n",
    "\n",
    "        return loss"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ab581173629b9cc",
   "metadata": {},
   "source": [
    "## 学习率衰减"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "39efc556d490f34e",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:31.228130Z",
     "start_time": "2025-02-06T14:04:31.223857Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-06T15:33:18.539022Z",
     "iopub.status.busy": "2025-02-06T15:33:18.538855Z",
     "iopub.status.idle": "2025-02-06T15:33:18.542374Z",
     "shell.execute_reply": "2025-02-06T15:33:18.541830Z",
     "shell.execute_reply.started": "2025-02-06T15:33:18.539007Z"
    }
   },
   "outputs": [],
   "source": [
    "# NoamDecayScheduler 是一个自定义或外部定义的学习率衰减调度器类。\n",
    "# 它需要接收配置 config 作为参数，可能实现了特定的学习率衰减方案\n",
    "class NoamDecayScheduler:\n",
    "    def __init__(self, config):\n",
    "        self.d_model = config[\"d_model\"]\n",
    "        self.warmup_steps = config[\"warmup_steps\"]\n",
    "\n",
    "    # __call__ 方法是一个特殊的方法，用于使对象能够像函数一样被调用\n",
    "    def __call__(self, step):\n",
    "        step += 1\n",
    "        arg1 = step ** (-0.5)  # 4000步之后是arg1\n",
    "        arg2 = step * (self.warmup_steps ** (-1.5))  # 4000步之前是arg2\n",
    "        arg3 = self.d_model ** (-0.5)\n",
    "\n",
    "        return arg3 * np.minimum(arg1, arg2)  # minimum是取两个数中的较小值\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "3188d631aaa09057",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:31.334627Z",
     "start_time": "2025-02-06T14:04:31.229133Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-06T15:33:18.542999Z",
     "iopub.status.busy": "2025-02-06T15:33:18.542841Z",
     "iopub.status.idle": "2025-02-06T15:33:18.630804Z",
     "shell.execute_reply": "2025-02-06T15:33:18.630217Z",
     "shell.execute_reply.started": "2025-02-06T15:33:18.542983Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "temp_learning_rate_schedule = NoamDecayScheduler({\"d_model\": 512, \"warmup_steps\": 4000})\n",
    "#下面是学习率的设计图\n",
    "plt.plot(temp_learning_rate_schedule(np.arange(0, 40000)))\n",
    "plt.ylabel(\"Leraning rate\")\n",
    "plt.xlabel(\"Train step\")\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "90185dd5b91d52ad",
   "metadata": {},
   "source": [
    "## 优化器"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "a5061e4cd6464c1b",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:31.339984Z",
     "start_time": "2025-02-06T14:04:31.335629Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-06T15:33:18.631928Z",
     "iopub.status.busy": "2025-02-06T15:33:18.631442Z",
     "iopub.status.idle": "2025-02-06T15:33:18.635754Z",
     "shell.execute_reply": "2025-02-06T15:33:18.635246Z",
     "shell.execute_reply.started": "2025-02-06T15:33:18.631898Z"
    }
   },
   "outputs": [],
   "source": [
    "from torch.optim.lr_scheduler import LambdaLR\n",
    "from torch.optim import Adam\n",
    "\n",
    "\n",
    "def get_optimizer(model, config):\n",
    "    base_lr = 0.1\n",
    "    beta1 = config[\"beta1\"]  # Adam 的 beta1\n",
    "    beta2 = config[\"beta2\"]  # Adam 的 beta2\n",
    "    eps = config[\"eps\"]\n",
    "    optimizer = Adam(model.parameters(), lr=base_lr, betas=(beta1, beta2), eps=eps)\n",
    "    # config是一个字典，包含了学习率衰减的参数\n",
    "    lr_scheduler = NoamDecayScheduler(config)\n",
    "    # 使用 LambdaLR 调度器，它可以根据给定的函数 lr_lambda 调整学习率。\n",
    "    # 这里将 lr_scheduler 作为函数传递给 LambdaLR，它包含了特定于模型或任务的学习率调度规则\n",
    "    scheduler = LambdaLR(optimizer, lr_lambda=lr_scheduler)\n",
    "    return optimizer, scheduler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "49a2f5433a409089",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:31.346703Z",
     "start_time": "2025-02-06T14:04:31.340986Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-06T15:33:18.636416Z",
     "iopub.status.busy": "2025-02-06T15:33:18.636235Z",
     "iopub.status.idle": "2025-02-06T15:33:18.640829Z",
     "shell.execute_reply": "2025-02-06T15:33:18.640393Z",
     "shell.execute_reply.started": "2025-02-06T15:33:18.636401Z"
    }
   },
   "outputs": [],
   "source": [
    "class SaveCheckpointsCallback:\n",
    "    def __init__(self, save_dir, save_step=5000, save_best_only=True):\n",
    "        self.save_dir = save_dir  # 保存路径\n",
    "        self.save_step = save_step  # 保存步数\n",
    "        self.save_best_only = save_best_only  # 是否只保存最好的模型\n",
    "        self.best_metric = -np.inf  # 最好的指标，指标不可能为负数，所以初始化为-1\n",
    "        # 创建保存路径\n",
    "        if not os.path.exists(self.save_dir):  # 如果不存在保存路径，则创建\n",
    "            os.makedirs(self.save_dir)\n",
    "\n",
    "    # 对象被调用时：当你将对象像函数一样调用时，Python 会自动调用 __call__ 方法。\n",
    "    # state_dict() 返回模型参数的字典，包括模型参数和优化器参数\n",
    "    # metric 是指标，可以是验证集的准确率，也可以是其他指标\n",
    "    def __call__(self, step, state_dict, metric=None):\n",
    "        if step % self.save_step > 0:\n",
    "            return  # 不是保存步数，则直接返回\n",
    "\n",
    "        if self.save_best_only:\n",
    "            assert metric is not None  # 必须传入metric\n",
    "            if metric >= self.best_metric:  # 如果当前指标大于最好的指标\n",
    "                # save checkpoint\n",
    "                # 保存最好的模型，覆盖之前的模型，不保存step，只保存state_dict，即模型参数，不保存优化器参数\n",
    "                torch.save(state_dict, os.path.join(self.save_dir, \"origin.ckpt\"))\n",
    "                self.best_metric = metric  # 更新最好的指标\n",
    "        else:\n",
    "            # 保存模型\n",
    "            torch.save(state_dict, os.path.join(self.save_dir, f\"{step}.ckpt\"))\n",
    "            # 保存每个step的模型，不覆盖之前的模型，保存step，保存state_dict，即模型参数，不保存优化器参数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "1c98380e43240291",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:31.352748Z",
     "start_time": "2025-02-06T14:04:31.347708Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-06T15:33:18.641549Z",
     "iopub.status.busy": "2025-02-06T15:33:18.641379Z",
     "iopub.status.idle": "2025-02-06T15:33:18.645236Z",
     "shell.execute_reply": "2025-02-06T15:33:18.644712Z",
     "shell.execute_reply.started": "2025-02-06T15:33:18.641534Z"
    }
   },
   "outputs": [],
   "source": [
    "class EarlyStopCallback:\n",
    "    def __init__(self, patience=5, min_delta=0.01):\n",
    "        self.patience = patience  # 多少个step没有提升就停止训练\n",
    "        self.min_delta = min_delta  # 最小的提升幅度\n",
    "        self.best_metric = -np.inf  # 记录的最好的指标\n",
    "        self.counter = 0  # 计数器，记录连续多少个step没有提升\n",
    "\n",
    "    def __call__(self, metric):\n",
    "        if metric >= self.best_metric + self.min_delta:  # 如果指标提升了\n",
    "            self.best_metric = metric  # 更新最好的指标\n",
    "            self.counter = 0  # 计数器清零\n",
    "        else:\n",
    "            self.counter += 1  # 计数器加一\n",
    "\n",
    "    @property  # 使用@property装饰器，使得 对象.early_stop可以调用，不需要()\n",
    "    def early_stop(self):\n",
    "        # 如果计数器大于等于patience，则返回True，停止训练\n",
    "        return self.counter >= self.patience"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ebb3c8c230d312c1",
   "metadata": {},
   "source": [
    "## training & valuating"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "140f2acfbeb9740a",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:31.358185Z",
     "start_time": "2025-02-06T14:04:31.353752Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-06T15:33:18.645851Z",
     "iopub.status.busy": "2025-02-06T15:33:18.645711Z",
     "iopub.status.idle": "2025-02-06T15:33:18.649570Z",
     "shell.execute_reply": "2025-02-06T15:33:18.649120Z",
     "shell.execute_reply.started": "2025-02-06T15:33:18.645837Z"
    }
   },
   "outputs": [],
   "source": [
    "@torch.no_grad()  # 禁用梯度计算\n",
    "def evaluating(model, data_loader, loss_fct):\n",
    "    loss_list = []\n",
    "    for batch in data_loader:\n",
    "        encoder_inputs = batch[\"encoder_inputs\"]\n",
    "        encoder_inputs_mask = batch[\"encoder_inputs_mask\"]\n",
    "        decoder_inputs = batch[\"decoder_inputs\"]\n",
    "        decoder_labels = batch[\"decoder_labels\"]\n",
    "        decoder_labels_mask = batch[\"decoder_labels_mask\"]\n",
    "\n",
    "        # 前向计算\n",
    "        outputs = model(\n",
    "            encoder_inputs=encoder_inputs,\n",
    "            decoder_inputs=decoder_inputs,\n",
    "            encoder_inputs_mask=encoder_inputs_mask,\n",
    "        )\n",
    "        logits = outputs.logits\n",
    "        # 计算损失\n",
    "        loss = loss_fct(logits, decoder_labels, padding_mask=decoder_labels_mask)\n",
    "        loss_list.append(loss.cpu().item())\n",
    "\n",
    "    return np.mean(loss_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "3099e48677dcb6bf",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:31.366317Z",
     "start_time": "2025-02-06T14:04:31.359188Z"
    },
    "ExecutionIndicator": {
     "show": true
    },
    "execution": {
     "iopub.execute_input": "2025-02-06T15:33:18.650217Z",
     "iopub.status.busy": "2025-02-06T15:33:18.650067Z",
     "iopub.status.idle": "2025-02-06T15:33:18.657510Z",
     "shell.execute_reply": "2025-02-06T15:33:18.656897Z",
     "shell.execute_reply.started": "2025-02-06T15:33:18.650202Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def training(model,\n",
    "             train_loader,\n",
    "             val_loader,\n",
    "             epoch,\n",
    "             loss_fct,\n",
    "             optimizer,\n",
    "             scheduler=None,\n",
    "             save_ckpt_callback=None,\n",
    "             early_stop_callback=None,\n",
    "             eval_step=500,\n",
    "             ):\n",
    "    record_dict = {\n",
    "        \"train\": [],\n",
    "        \"val\": []\n",
    "    }\n",
    "\n",
    "    global_step = 1  # 全局步数\n",
    "    model.train()  # 训练模式\n",
    "    with tqdm(total=epoch * len(train_loader)) as pbar:\n",
    "        for epoch_id in range(epoch):\n",
    "            # 训练\n",
    "            for batch in train_loader:\n",
    "                encoder_inputs = batch[\"encoder_inputs\"]\n",
    "                encoder_inputs_mask = batch[\"encoder_inputs_mask\"]\n",
    "                decoder_inputs = batch[\"decoder_inputs\"]\n",
    "                decoder_labels = batch[\"decoder_labels\"]\n",
    "                decoder_labels_mask = batch[\"decoder_labels_mask\"]\n",
    "\n",
    "                optimizer.zero_grad()  # 梯度清零\n",
    "\n",
    "                # 前向传播\n",
    "                outputs = model(\n",
    "                    encoder_inputs=encoder_inputs,\n",
    "                    decoder_inputs=decoder_inputs,\n",
    "                    encoder_inputs_mask=encoder_inputs_mask\n",
    "                )\n",
    "                logits = outputs.logits\n",
    "                loss = loss_fct(logits, decoder_labels, padding_mask=decoder_labels_mask)\n",
    "\n",
    "                # 梯度回传\n",
    "                loss.backward()\n",
    "\n",
    "                # 调整优化器，包括学习率的变动等\n",
    "                optimizer.step()\n",
    "                if scheduler is not None:\n",
    "                    scheduler.step()  # 更新学习率\n",
    "\n",
    "                loss = loss.cpu().item()\n",
    "\n",
    "                record_dict[\"train\"].append({\n",
    "                    \"loss\": loss,\n",
    "                    \"step\": global_step\n",
    "                })\n",
    "\n",
    "                # 评估\n",
    "                if global_step % eval_step == 0:\n",
    "                    model.eval()  # 评估模式\n",
    "                    # 验证集损失和准确率\n",
    "                    val_loss = evaluating(model, val_loader, loss_fct)\n",
    "                    record_dict[\"val\"].append({\n",
    "                        \"loss\": val_loss,\n",
    "                        \"step\": global_step\n",
    "                    })\n",
    "                    model.train()  # 训练模式\n",
    "\n",
    "                    # 2. 保存模型权重 save model checkpoint\n",
    "                    if save_ckpt_callback is not None:\n",
    "                        # model.state_dict() 返回模型参数的字典，包括模型参数和优化器参数\n",
    "                        save_ckpt_callback(global_step, model.state_dict(), -val_loss)\n",
    "                        # 保存最好的模型，覆盖之前的模型，保存step，保存state_dict,通过metric判断是否保存最好的模型\n",
    "\n",
    "                    # 3. 早停 early stopping\n",
    "                    if early_stop_callback is not None:\n",
    "                        # 验证集准确率不再提升，则停止训练\n",
    "                        early_stop_callback(-val_loss)\n",
    "                        # 验证集准确率不再提升，则停止训练\n",
    "                        if early_stop_callback.early_stop:\n",
    "                            print(f\"Early stop at epoch {epoch_id} / global_step {global_step}\")\n",
    "                            return record_dict  # 早停，返回记录字典 record_dict\n",
    "\n",
    "                # 更新进度条和全局步数\n",
    "                pbar.update(1)  # 更新进度条\n",
    "                global_step += 1  # 全局步数加一\n",
    "            pbar.set_postfix({\"epoch\": epoch_id, \"loss\": loss, \"val_loss\": val_loss})\n",
    "\n",
    "    return record_dict  # 训练结束，返回记录字典 record_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "cf04ac5e18afea4f",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:31.441762Z",
     "start_time": "2025-02-06T14:04:31.367320Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-06T15:33:18.658088Z",
     "iopub.status.busy": "2025-02-06T15:33:18.657944Z",
     "iopub.status.idle": "2025-02-06T15:33:18.815815Z",
     "shell.execute_reply": "2025-02-06T15:33:18.815248Z",
     "shell.execute_reply.started": "2025-02-06T15:33:18.658073Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load train dataset from wmt16/.cache/de2en_train_128.npy\n",
      "load val dataset from wmt16/.cache/de2en_val_128.npy\n"
     ]
    }
   ],
   "source": [
    "#模型的超参\n",
    "config = {\n",
    "    \"bos_idx\": 1,\n",
    "    \"eos_idx\": 3,\n",
    "    \"pad_idx\": 0,\n",
    "    \"vocab_size\": len(word2idx),\n",
    "    \"max_length\": 128,\n",
    "    \"d_model\": 512, # 可调整\n",
    "    \"dim_feedforward\": 2048,  # FFN 的隐藏层大小\n",
    "    \"dropout\": 0.1, # 可调整\n",
    "    \"layer_norm_eps\": 1e-6,  # 层归一化的 epsilon, 防止除零错误\n",
    "    \"num_heads\": 8, # 可调整\n",
    "    \"num_decoder_layers\": 6, # 可调整\n",
    "    \"num_encoder_layers\": 6, # 可调整\n",
    "    \"label_smoothing\": 0.1,\n",
    "    \"beta1\": 0.9,  # Adam 的 beta1\n",
    "    \"beta2\": 0.98,  # Adam 的 beta2\n",
    "    \"eps\": 1e-9,\n",
    "    \"warmup_steps\": 4_000,\n",
    "    \"share_embedding\": False,  # 是否共享词向量\n",
    "}\n",
    "\n",
    "\n",
    "def get_dl(dataset, batch_size, shuffle=True):\n",
    "    sampler = TransformerBatchSampler(dataset, batch_size=batch_size, shuffle_batch=shuffle)\n",
    "    sample_dl = DataLoader(dataset, batch_sampler=sampler, collate_fn=partial(collate_fn, tokenizer=tokenizer))\n",
    "    return sample_dl\n",
    "\n",
    "\n",
    "# dataset\n",
    "train_ds = LangPairDataset(\"train\", max_length=config[\"max_length\"])\n",
    "val_ds = LangPairDataset(\"val\", max_length=config[\"max_length\"])\n",
    "\n",
    "# tokenizer\n",
    "tokenizer = Tokenizer(word2idx=word2idx, idx2word=idx2word, max_length=config[\"max_length\"])\n",
    "\n",
    "# dataloader\n",
    "batch_size = 2048\n",
    "train_dl = get_dl(train_ds, batch_size=batch_size, shuffle=True)\n",
    "val_dl = get_dl(val_ds, batch_size=batch_size, shuffle=False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "c4bd1cb45f5032ee",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:32.221751Z",
     "start_time": "2025-02-06T14:04:31.442764Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-06T15:33:18.816524Z",
     "iopub.status.busy": "2025-02-06T15:33:18.816344Z",
     "iopub.status.idle": "2025-02-06T15:33:19.547977Z",
     "shell.execute_reply": "2025-02-06T15:33:19.547510Z",
     "shell.execute_reply.started": "2025-02-06T15:33:18.816507Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "模型参数量: 71938239\n"
     ]
    }
   ],
   "source": [
    "#计算模型参数量\n",
    "model = TransformerModel(config)\n",
    "print(f\"模型参数量: {sum(p.numel() for p in model.parameters() if p.requires_grad)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "6b87e4917b9ef89b",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:53.810386Z",
     "start_time": "2025-02-06T14:04:32.224757Z"
    },
    "ExecutionIndicator": {
     "show": true
    },
    "execution": {
     "iopub.execute_input": "2025-02-06T15:33:19.548724Z",
     "iopub.status.busy": "2025-02-06T15:33:19.548498Z",
     "iopub.status.idle": "2025-02-06T15:53:32.016771Z",
     "shell.execute_reply": "2025-02-06T15:53:32.016164Z",
     "shell.execute_reply.started": "2025-02-06T15:33:19.548708Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "29999it [20:10, 24.77it/s, epoch=29, step=3e+4, loss=3.07]                        "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Early stop at epoch 28 / global_step 30000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "epoch = 100\n",
    "\n",
    "# model\n",
    "model = TransformerModel(config)\n",
    "model = model.to(device)\n",
    "\n",
    "# 1. 定义损失函数 采用交叉熵损失\n",
    "loss_fct = CrossEntropyWithPadding(config)\n",
    "\n",
    "# 2. 定义优化器\n",
    "optimizer, scheduler = get_optimizer(model, config)\n",
    "\n",
    "# 3.save model checkpoint\n",
    "if not os.path.exists(\"checkpoints\"):\n",
    "    os.makedirs(\"checkpoints\")\n",
    "save_ckpt_callback = SaveCheckpointsCallback(save_dir=\"checkpoints\", save_step=500, save_best_only=True)\n",
    "\n",
    "# 4. early stopping\n",
    "early_stop_callback = EarlyStopCallback(patience=10,min_delta=0.001)\n",
    "\n",
    "record_dict = training(\n",
    "    model,\n",
    "    train_dl,\n",
    "    val_dl,\n",
    "    epoch,\n",
    "    loss_fct,\n",
    "    optimizer,\n",
    "    scheduler,\n",
    "    save_ckpt_callback=save_ckpt_callback,\n",
    "    early_stop_callback=early_stop_callback,\n",
    "    eval_step=500\n",
    ")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "50ac7e66d5df05c3",
   "metadata": {},
   "source": [
    "# 推理\n",
    "\n",
    "- 翻译项目的评估指标一般是BLEU4\n",
    "- 接下来进行翻译推理，并作出注意力的热度图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "305c0410623a9f8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-02-06T15:53:32.018469Z",
     "iopub.status.busy": "2025-02-06T15:53:32.017837Z",
     "iopub.status.idle": "2025-02-06T15:53:32.149155Z",
     "shell.execute_reply": "2025-02-06T15:53:32.148710Z",
     "shell.execute_reply.started": "2025-02-06T15:53:32.018435Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_910/4017629773.py:4: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  state_dict = torch.load(f\"checkpoints/origin.ckpt\", map_location=\"cpu\")\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "\n",
    "# 加载模型\n",
    "state_dict = torch.load(f\"checkpoints/origin.ckpt\", map_location=\"cpu\",weights_only=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "6f512e7c8f2db972",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-02-06T15:53:32.150064Z",
     "iopub.status.busy": "2025-02-06T15:53:32.149704Z",
     "iopub.status.idle": "2025-02-06T15:53:43.036199Z",
     "shell.execute_reply": "2025-02-06T15:53:43.035757Z",
     "shell.execute_reply.started": "2025-02-06T15:53:32.150047Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "save cache to wmt16/.cache/de2en_test_128.npy\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "0it [00:00, ?it/s]/usr/local/lib/python3.10/site-packages/nltk/translate/bleu_score.py:577: UserWarning: \n",
      "The hypothesis contains 0 counts of 4-gram overlaps.\n",
      "Therefore the BLEU score evaluates to 0, independently of\n",
      "how many N-gram overlaps of lower order it contains.\n",
      "Consider using lower n-gram order or use SmoothingFunction()\n",
      "  warnings.warn(_msg)\n",
      "8it [00:00, 77.81it/s]/usr/local/lib/python3.10/site-packages/nltk/translate/bleu_score.py:577: UserWarning: \n",
      "The hypothesis contains 0 counts of 3-gram overlaps.\n",
      "Therefore the BLEU score evaluates to 0, independently of\n",
      "how many N-gram overlaps of lower order it contains.\n",
      "Consider using lower n-gram order or use SmoothingFunction()\n",
      "  warnings.warn(_msg)\n",
      "30it [00:00, 97.32it/s]/usr/local/lib/python3.10/site-packages/nltk/translate/bleu_score.py:577: UserWarning: \n",
      "The hypothesis contains 0 counts of 2-gram overlaps.\n",
      "Therefore the BLEU score evaluates to 0, independently of\n",
      "how many N-gram overlaps of lower order it contains.\n",
      "Consider using lower n-gram order or use SmoothingFunction()\n",
      "  warnings.warn(_msg)\n",
      "966it [00:09, 100.82it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "testing loss: 3.3431327489839084\n",
      "testing bleu: 0.5842845058848077\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "from nltk.translate.bleu_score import sentence_bleu\n",
    "\n",
    "model = TransformerModel(config)\n",
    "model.load_state_dict(state_dict)\n",
    "\n",
    "loss_fct = CrossEntropyWithPadding(config)\n",
    "\n",
    "test_ds = LangPairDataset(\"test\", max_length=config[\"max_length\"])\n",
    "test_dl = DataLoader(\n",
    "    test_ds, batch_size=1,\n",
    "    collate_fn=partial(collate_fn, tokenizer=tokenizer)\n",
    ")\n",
    "\n",
    "model = model.to(device)\n",
    "model.eval()\n",
    "collect = {}\n",
    "loss_collect = []\n",
    "\n",
    "predictions = []\n",
    "answers = []\n",
    "\n",
    "# 初始化BLEU分数列表\n",
    "bleu_scores = []\n",
    "for idx, batch in tqdm(enumerate(test_dl)):\n",
    "    encoder_inputs = batch[\"encoder_inputs\"]\n",
    "    encoder_inputs_mask = batch[\"encoder_inputs_mask\"]\n",
    "    decoder_inputs = batch[\"decoder_inputs\"]\n",
    "    decoder_labels = batch[\"decoder_labels\"]\n",
    "\n",
    "    # 前向计算\n",
    "    outputs = model(\n",
    "        encoder_inputs=encoder_inputs,\n",
    "        decoder_inputs=decoder_inputs,\n",
    "        encoder_inputs_mask=encoder_inputs_mask\n",
    "    )\n",
    "\n",
    "    loss = loss_fct(outputs.logits, decoder_labels)  # 验证集损失\n",
    "\n",
    "    preds = outputs.logits.argmax(dim=-1)  # 预测结果，[1,seq_len]\n",
    "    # 把preds转为英文单词\n",
    "    preds = tokenizer.decode(preds.cpu().numpy())  #['预测句子']\n",
    "    #把decoder_labels转为英文单词\n",
    "    decoder_labels = tokenizer.decode(decoder_labels.cpu().numpy())  #['标签句子']\n",
    "\n",
    "    answers.append(decoder_labels)\n",
    "\n",
    "    belu = sentence_bleu([decoder_labels[0].split()], preds[0].split(), weights=(1, 0, 0, 0))\n",
    "    bleu_scores.append(belu)\n",
    "    collect[idx] = {\n",
    "        \"loss\": loss.item(),\n",
    "        \"src_inputs\": encoder_inputs,\n",
    "        \"trg_inputs\": decoder_inputs,\n",
    "        \"mask\": encoder_inputs_mask,\n",
    "        \"trg_labels\": decoder_labels,\n",
    "        \"preds\": preds\n",
    "    }\n",
    "    loss_collect.append(loss.item())\n",
    "\n",
    "# sort collect by value\n",
    "collect = sorted(collect.items(), key=lambda x: x[1][\"loss\"])\n",
    "print(f\"testing loss: {np.array(loss_collect).mean()}\")\n",
    "belu_scores = sum(bleu_scores) / len(bleu_scores)\n",
    "print(f\"testing bleu: {belu_scores}\")"
   ]
  },
  {
   "metadata": {},
   "cell_type": "code",
   "outputs": [],
   "execution_count": null,
   "source": [
    "# !pip install Cython  # if failed to install fastBPE, try this line\n",
    "# !pip install fastBPE -v\n",
    "# 在 Windows 系统上并没有 sys/mman.h 文件"
   ],
   "id": "a9ab2039d24b72a3"
  },
  {
   "metadata": {},
   "cell_type": "code",
   "outputs": [],
   "execution_count": null,
   "source": [
    "import re\n",
    "from fastBPE import fastBPE\n",
    "from sacremoses import MosesDetokenizer, MosesTokenizer\n",
    "\n",
    "# `MosesTokenizer` 和 `MosesDetokenizer` 是来自 `sacremoses` 库的工具，用于自然语言处理中的分词（Tokenization）和去标记化（Detokenization）。\n",
    "# 这些工具主要用于对文本进行预处理和后处理，通常在处理自然语言处理任务时会用到。\n",
    "\n",
    "# 这些工具通常在文本预处理和后处理过程中使用，对输入的文本进行标记化和去标记化，是一种常用的处理方式。\n",
    "# 在自然语言处理任务中，对文本进行正确的分词和还原是很重要的，而 `MosesTokenizer` 和 `MosesDetokenizer` 提供了方便、高效的工具来处理这些任务。\n",
    "\n",
    "class Translator:\n",
    "    def __init__(self, model, src_tokenizer, trg_tokenizer):\n",
    "        self.bpe = fastBPE(\"./wmt16/bpe.20000\", \"./wmt16/vocab\")\n",
    "        self.mose_tokenizer = MosesTokenizer(lang=\"de\")\n",
    "        self.mose_detokenizer = MosesDetokenizer(lang=\"en\")\n",
    "        self.model = model\n",
    "        self.model.eval()\n",
    "        self.src_tokenizer = src_tokenizer\n",
    "        self.trg_tokenizer = trg_tokenizer\n",
    "        # 匹配出现的 @@ （后面带空格的情况）。\n",
    "        # 或者匹配以 @@ 结尾的情况（可选空格）。\n",
    "        self.pattern = re.compile(r'(@@ )|(@@ ?$)')\n",
    "        \n",
    "    def draw_attention_map(self, attn_scores, cross_attn_scores, src_words_list, trg_words_list):\n",
    "        \"\"\"绘制注意力热力图\n",
    "        attn_scores (numpy.ndarray): 表示自注意力机制（self-attention）分数。\n",
    "        cross_attn_scores (numpy.ndarray): 表示交叉注意力机制的注意力分数。\n",
    "        src_words_list (list): 源语言句子的单词列表。\n",
    "        trg_words_list (list): 目标语言句子的单词列表。\n",
    "        \"\"\"\n",
    "        assert len(attn_scores.shape) == 3, \"attn_scores shape should be \" \\\n",
    "            f\"[num heads, target sequence length, target sequence length], but got {attn_scores.shape}\"\n",
    "        attn_scores = attn_scores[:, :len(trg_words_list), :len(trg_words_list)]\n",
    "\n",
    "        assert len(cross_attn_scores.shape) == 3, \"attn_scores shape should be \" \\\n",
    "            f\"[num heads, target sequence length, source sequence length], but got {cross_attn_scores.shape}\"\n",
    "        cross_attn_scores = cross_attn_scores[:, :len(trg_words_list), :len(src_words_list)]\n",
    "\n",
    "        num_heads, trg_len, src_len = cross_attn_scores.shape\n",
    "\n",
    "        fig = plt.figure(figsize=(10, 5), constrained_layout=True) # constrained_layout=True 自动调整子图参数，使之填充整个图像区域\n",
    "        grid = plt.GridSpec(trg_len, trg_len + src_len, wspace=0.1, hspace=0.1)# wspace,hspace 控制子图之间的间距\n",
    "        #下面是attn_scores的热力图\n",
    "        self_map = fig.add_subplot(grid[:,:trg_len]) #  添加子图\n",
    "        self_map.matshow(attn_scores.mean(dim=0), cmap='viridis') # 绘制热力图，cmap表示颜色,dim=0表示对第0维求均值\n",
    "        self_map.set_yticks(range(trg_len), trg_words_list, fontsize=10)\n",
    "        self_map.set_xticks(range(trg_len), [\"[BOS]\"] + trg_words_list[:-1], rotation=90)\n",
    "        #下面是cross_attn_scores的热力图\n",
    "        cross_map = fig.add_subplot(grid[:, trg_len:])\n",
    "        cross_map.matshow(cross_attn_scores.mean(dim=0), cmap='viridis')\n",
    "        cross_map.set_yticks(range(trg_len), [], fontsize=6)\n",
    "        cross_map.set_xticks(range(src_len), src_words_list, rotation=90)\n",
    "\n",
    "        plt.show()\n",
    "\n",
    "    def draw_attention_maps(self, attn_scores, cross_attn_scores, src_words_list, trg_words_list, heads_list):\n",
    "        \"\"\"绘制注意力热力图\n",
    "\n",
    "        Args:\n",
    "            - scores (numpy.ndarray): shape = [source sequence length, target sequence length]\n",
    "        \"\"\"\n",
    "        assert len(attn_scores.shape) == 3, \"attn_scores shape should be \" \\\n",
    "            f\"[num heads, target sequence length, target sequence length], but got {attn_scores.shape}\"\n",
    "        attn_scores = attn_scores[:, :len(trg_words_list), :len(trg_words_list)]\n",
    "\n",
    "        assert len(cross_attn_scores.shape) == 3, \"attn_scores shape should be \" \\\n",
    "            f\"[num heads, target sequence length, source sequence length], but got {cross_attn_scores.shape}\"\n",
    "        cross_attn_scores = cross_attn_scores[:, :len(trg_words_list), :len(src_words_list)]\n",
    "        # cross_attn_scores = cross_attn_scores[:, :len(src_words_list), :len(src_words_list)]\n",
    "\n",
    "        num_heads, trg_len, src_len = cross_attn_scores.shape\n",
    "        fig, axes = plt.subplots(2, len(heads_list), figsize=(5 * len(heads_list), 10))\n",
    "        for i, heads_idx in enumerate(heads_list):\n",
    "            axes[0, i].matshow(attn_scores[heads_idx], cmap='viridis')\n",
    "            axes[0, i].set_yticks(range(trg_len), trg_words_list)\n",
    "            axes[0, i].set_xticks(range(trg_len), [\"[BOS]\"] + trg_words_list[:-1], rotation=90)\n",
    "            axes[0, i].set_title(f\"head {heads_idx}\")\n",
    "            axes[1, i].matshow(cross_attn_scores[heads_idx], cmap='viridis')\n",
    "            axes[1, i].set_yticks(range(trg_len), trg_words_list)\n",
    "            axes[1, i].set_xticks(range(src_len), src_words_list, rotation=90)\n",
    "            axes[1, i].set_title(f\"head {heads_idx}\")\n",
    "\n",
    "        plt.show()\n",
    "    \n",
    "    def __call__(self, sentence_list,heads_list=None,layer_idx=-1):\n",
    "        # 将输入句子列表转换为小写，并使用 MosesTokenizer 进行分词处理。\n",
    "        sentence_list = [\" \".join(self.mose_tokenizer.tokenize(s.lower())) for s in sentence_list]\n",
    "        # 将分词后的结果进行 BPE 编码，得到 tokens_list。\n",
    "        tokens_list = [s.split() for s in self.bpe.apply(sentence_list)]\n",
    "        \n",
    "        # 使用 src_tokenizer 对 tokens_list 进行编码，同时添加起始标记 ([BOS]) 和结束标记 ([EOS])。\n",
    "        encoder_input, attn_mask = self.src_tokenizer.encode(\n",
    "            tokens_list,\n",
    "            add_bos=True,\n",
    "            add_eos=True,\n",
    "            return_mask=True,\n",
    "            )\n",
    "        encoder_input = torch.Tensor(encoder_input).to(dtype=torch.int64)\n",
    "        \n",
    "        # 使用模型的 infer 方法对编码器输入进行推理，得到输出结果 outputs\n",
    "        outputs = model.infer(encoder_inputs=encoder_input, encoder_inputs_mask=attn_mask)\n",
    "        preds = outputs.preds.numpy() # 推理结果\n",
    "        \n",
    "        # 使用目标语言的 trg_tokenizer 对预测序列进行解码，得到解码后的目标语言句子列表 trg_decoded。\n",
    "        trg_decoded = self.trg_tokenizer.decode(preds, split=True, remove_eos=False, remove_bos=False, remove_pad=False)\n",
    "        # 使用源语言的 src_tokenizer 对编码器输入进行解码，得到解码后的源语言句子列表 src_decoded。为下面绘制热力图做准备。\n",
    "        src_decoded = self.src_tokenizer.decode(\n",
    "            encoder_input.numpy(),\n",
    "            split=True,\n",
    "            remove_bos=False,\n",
    "            remove_eos=False\n",
    "            )\n",
    "        \n",
    "         # draw the attention map of the last decoder block\n",
    "        for attn_score, cross_attn_score, src, trg in zip(\n",
    "            outputs.decoder_self_attn_scores[layer_idx], outputs.decoder_cross_attn_scores[layer_idx], src_decoded, trg_decoded):\n",
    "            if heads_list is None:# 如果没有指定heads_list，就画单个热力图\n",
    "                self.draw_attention_map(\n",
    "                    attn_score,\n",
    "                    cross_attn_score,\n",
    "                    src,\n",
    "                    trg,\n",
    "                )\n",
    "            else:# 如果指定了heads_list，就画多个热力图\n",
    "                self.draw_attention_maps(\n",
    "                    attn_score,\n",
    "                    cross_attn_score,\n",
    "                    src,\n",
    "                    trg,\n",
    "                    heads_list=heads_list,\n",
    "                    )\n",
    "        \n",
    "        #将解码后的目标语言句子列表返回，并使用 mose_detokenizer 进行去标记化，最终得到翻译后的结果。\n",
    "        return [self.mose_detokenizer.tokenize(self.pattern.sub(\"\", s).split()) for s in self.trg_tokenizer.decode(preds)] \n",
    "        "
   ],
   "id": "1e7c1e0999eb365d"
  },
  {
   "metadata": {},
   "cell_type": "code",
   "outputs": [],
   "execution_count": null,
   "source": [
    "sentence_list = [\n",
    "    \"Mann in einem kleinen weißen Boot auf einem See.\",  # Man in a small white boat on a lake.\n",
    "    # \"Ein Mann mit einem Eimer und ein Mädchen mit einem Hut am Strand.\", # A man with a bucket and a girl in a hat on the beach.\n",
    "    # \"Drei Männer auf Pferden während eines Rennens.\",  # Three men on horses during a race.\n",
    "    # \"Ein Mann und eine Frau essen zu Abend\",  # 一个男人和一个女人在吃晚餐\n",
    "]\n",
    "\n",
    "# load checkpoints\n",
    "model = TransformerModel(config)\n",
    "model.load_state_dict(state_dict)\n",
    "translator = Translator(model.cpu(), tokenizer, tokenizer)\n",
    "translator(\n",
    "    sentence_list,\n",
    "    layer_idx=-1,\n",
    "    # heads_list=[0, 1, 2, 3, 4, 5, 6, 7]\n",
    "    )"
   ],
   "id": "c5d288cf83d67b62"
  }
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